ai engineer

General Dynamics Information Technology

<p></p><div><div><div><p>Your Impact</p><p>
Own your opportunity to work alongside federal civilian agencies. Make an impact by providing services that help the government ensure the well being and support of U.S. citizens.</p><p>
Job Description</p><div><p>
Own your opportunity to shape the future of AI at GDIT<br>
As an AI Engineer supporting the mission of the United States Postal Service (USPS), you will help design, develop, and deploy AI solutions that modernize and optimize essential nationwide operations. Your work will directly contribute to improved efficiency, automated processes, and data‑driven decision making across a high‑impact federal environment.</p><p></p><p>
MEANINGFUL WORK AND PERSONAL IMPACT<br>
As an AI Engineer, you will play a critical role in identifying where AI can streamline business processes and deliver measurable outcomes. You will:</p><ul><li>
Develop and advance AI capabilities across USPS business functions.</li><li>
Build tools that automate requirements and test case generation using technologies such as Visual Studio Code, Python, Microsoft Copilot, CrewAI, and other USPS‑approved platforms.</li><li>
Integrate AI solutions with existing automation frameworks to reduce manual effort and enhance quality.</li><li>
Evaluate opportunities to embed AI across enterprise workflows and design solutions aligned with mission needs.</li><li>
Collaborate with cross‑functional teams and communicate technical concepts to both technical and non‑technical stakeholders.</li><li>
Ensure all AI solutions meet USPS security, compliance, and execution requirements.</li><li>
Train designated team members to maintain and operate AI‑driven tools and scripts.</li></ul><p>
WHAT YOU&rsquo;LL NEED TO SUCCEED</p><p>
Education</p><ul><li>
Bachelor&rsquo;s degree in a related field with strong mathematics</li><li>
OR an additional four years of relevant professional experience in lieu of a degree.</li></ul><p>
Required Experience<br>
Candidates will be evaluated for one of the following levels:</p><ul><li>
Associate AI Engineer: 1&ndash;2 years of experience developing AI or automation solutions.</li><li>
Senior AI Engineer: 3&ndash;7 years of hands‑on AI development experience.</li><li>
Principal AI Engineer: 8+ years of experience leading or delivering AI, automation, or software development projects.</li></ul><p>
Additional expectations for all levels:</p><ul><li>
Proven experience contributing to or leading AI/automation initiatives.</li><li>
Experience working in analytics, data‑driven environments, or team leadership roles is highly desirable.</li><li>
Strong agentic AI experience, including familiarity with agent frameworks, orchestration tools, and automation workflows.</li><li>
Experience working across the infrastructure plane to ensure AI systems operate reliably within enterprise environments.</li><li>
Strong mathematical foundation in algebra, statistics, calculus, or related fields applied to AI/ML model development.</li><li>
Hands‑on experience developing, training, and evaluating AI or ML models.</li><li>
Experience performing optimization to improve model performance, scalability, or efficiency.</li><li>
Practical experience applying machine learning methods to real‑world business or operational challenges.</li></ul><p>
Required Technical Skills</p><ul><li>
Proficiency in building AI solutions using Python, Visual Studio Code, and relevant programming or scripting languages.</li><li>
Strong understanding of generative AI, LLM integration, and modern AI frameworks.</li><li>
Ability to develop AI‑driven tools that convert business user stories into technical requirements.</li><li>
Experience integrating AI capabilities into existing enterprise automation frameworks.</li><li>
Ability to deliver AI scripts and programs that run reliably on USPS‑approved environments.</li><li>
Capability to document, train, and transition AI tools to operational teams.</li></ul><p>
Preferred Skills</p><ul><li>
Strong analytical and problem‑solving skills with a creative approach to complex technical challenges.</li><li>
Excellent communication skills with the ability to explain sophisticated AI concepts to non‑technical stakeholders.</li><li>
Demonstrated ability to work in a collaborative, cross‑functional team environment.</li><li>
Familiarity with Agile methodologies, cloud platforms, or enterprise‑scale AI deployments is a plus.</li></ul><p>
Security Clearance Level:</p><ul><li>
Ability to obtain and maintain a Public Trust clearance and successfully pass a thorough Government background screening process requiring the completion of detailed forms and fingerprinting</li><li>
This position has a U.S. residency requirement. The USPS security clearance process requires the selected candidate to have resided in the U.S. (including U.S. Territories) for the last five years as follows: U.S. Citizens cannot have left the U.S. (including U.S. Territories) for longer than 6 months consecutively in the last 3 years (unless they meet certain exceptions). Non-U.S. Citizens cannot have left the U.S. (including U.S. Territories) for longer than 90 days consecutively in the last 3 years.</li></ul><p>
Location:</p><ul><li>
Remote</li></ul><p>
GDIT IS YOUR PLACE</p><p>
At GDIT, the mission is our purpose, and our people are at the center of everything we do.</p><ul><li>
Growth: AI-powered career tool that identifies career steps and learning opportunities</li><li>
Support: An internal mobility team focused on helping you achieve your career goals</li><li>
Rewards: Comprehensive benefits and wellness packages, 401K with company match, competitive pay and paid time off</li><li>
Community: Award-winning culture of innovation and a military-friendly workplace</li></ul><p>
OWN YOUR OPPORTUNITY<br>
Explore a career in data science and engineering at GDIT and you&rsquo;ll find endless opportunities to grow alongside colleagues who share your determination for solving complex data challenges.</p><p></p><p>
#zxc726 #AIEngineer</p>
</div></div><div><div><div><p>Work Requirements</p></div></div><div></div><div><div>
Years of Experience<div>
2 + years of related experience</div><ul><li>may vary based on technical training, certification(s), <i>or</i> degree</li></ul></div><div></div><div>
Certification</div><p></p><div>
Travel Required<div>
Less than 10%</div></div></div></div></div><div><p>
Salary and Benefit Information</p><div>
The likely salary range for this position is $111,155 - $150,385. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.<br>
View information about benefits and our total rewards program.</div><p>
About Our Work</p><div>
We are GDIT. A global technology and professional services company that delivers technology and mission services to every major agency across the U.S. government, defense and intelligence community. Our 26,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across over 50 countries worldwide, offering leading capabilities in digital modernization, AI/ML, cloud, cyber and application development. Together with our customers, we strive to create a safer, smarter world by harnessing the power of deep expertise and advanced technology.</div><div>
Join our Talent Community to stay up to date on our career opportunities and events at gdit.com/tc.</div><div><i>
Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans</i></div></div></div>

AI Engineer

Zoolatech

<div><div><div><div><div><div><div><div>OVERVIEW</div>
</div><div><div>RESPONSIBILITIES</div>
</div><div><div>REQUIREMENTS</div>
</div></div></div></div><div><div><div><p>ATRC's Data &amp; AI Office is a product and platform execution team &mdash; not a traditional enterprise data function. We build and operate AI-enabled systems that create measurable operational impact across the organization. Our primary output is working software in production.</p><p><b>
What we are building &mdash; now:</b></p><ul><li><p>
An executive reporting portal serving senior leadership, integrating live data from SAP, Microsoft 365, SuccessFactors, and internal systems across multiple operational views (finance &amp; budget, KPIs, technology portfolio, news, wellbeing, and a personalized executive home)</p></li><li><p>
A sovereign data and AI platform: data warehouse, governed pipelines, metadata catalog, and classification enforcement across all entities</p></li><li><p>
Data mesh architecture with federated ownership &mdash; each entity owns its data products; the platform provides governed access</p></li><li><p>
Data readiness for the organization's agentic AI platform &mdash; clean, structured, governed data so AI agents can operate on trusted sources</p></li><li><p>
Operational AI agents delivering real value on top of the agentic platform (document triage, anomaly detection, automated briefings, classification automation)</p></li><li><p>
AI-enabled situational awareness and crisis management systems</p></li><li><p>
Data governance and classification &mdash; managing an active external vendor engagement, bridging framework deliverables into platform-enforced rules, and building the ongoing</p><p>
governance operations the organization requires</p></li></ul><p><b>
How we work:</b></p><ul><li><p>
Small team, high ownership &mdash; every engineer owns a module end-to-end</p></li><li><p>
Two-week sprints with demonstrated output at every review</p></li><li><p>
Ship and improve &mdash; working systems beat perfect designs</p></li><li><p>
Core team owns all architecture and product decisions; external capacity executes within defined scope</p></li><li><p>
Decisions take hours, not weeks. Daily technical choices are made by the engineer.</p></li><li><p>
Feature scope decisions take 24 hours. Architecture pivots take 48 hours. Nothing sits in limbo.</p></li><li><p>
Oversee 5 other workstreams powered by 3rd party vendors within the ecosystem but whom we are ultimately accountable for</p></li></ul>
</div><div><p>Builds the AI capabilities that operations depend on &mdash; classification automation, production agents, and the data bridge to the agentic platform.</p><p><b>
Mission</b></p><p>
Build, own, and continuously improve the AI capabilities across the office's production systems, working under the direction of the Principal AI Engineer. Your scope: automated data classification (remediating what's currently broken), AI-generated briefings, and operational AI agents that deliver real value in weeks.</p><p>
An external partner is building an agentic platform that ATRC will use. Your role in that relationship: define the data contracts the platform consumes, evaluate the sample agents delivered, and build the production agents on top of the platform that go beyond demonstrations into real operational use. You don't build the platform &mdash; you make sure ATRC's data is ready for it and that what gets built on top of it actually works.</p><p><b>
What This Role Owns</b></p><ul><li><p>
Data classification automation: implementing automated classification to remediate current failures, embedding classification into data pipelines alongside the Governance</p></li><li><p>
Lead Operational AI agents: building production agents on top of the agentic platform &mdash; going beyond the sample agents the external partner delivers into real operational workflows</p></li><li><p>
Agentic platform data contracts: defining what data the platform needs, in what format, with what quality guarantees &mdash; working with the Principal AI Engineer</p></li><li><p>
AI service implementation: FastAPI service around LLM APIs with versioned prompt templates</p></li><li><p>
Classification and briefing prompts: structured prompts returning validated JSON with tags, confidence levels, source attribution</p></li><li><p>
Prompt versioning: templates in configuration, editable without code changes</p></li><li><p>
Observability: every LLM call logged with input hash, model version, output, latency, token count</p></li><li><p>
Fallback logic: graceful degradation when LLM APIs are unavailable</p></li><li><p>
Quality evaluation: running precision/recall evaluations against human reviewer samples, reporting results, iterating prompts</p></li></ul><p><b>
Key Decisions</b></p><ul><li><p>
Prompt implementation &mdash; few-shot examples, output parsing, error handling</p></li><li><p>
Confidence threshold tuning based on evaluation results</p></li><li><p>
Context window packing &mdash; what data fits within token budgets</p></li><li><p>
Fallback behavior when APIs degrade</p></li><li><p>
Agent evaluation &mdash; whether an external partner's sample agent is production-ready or needs rework</p></li><li><p>
Bug triage for AI components &mdash; autonomous fix vs. escalate to Principal AI Engineer</p></li></ul><p><b>
Does Not Do</b></p><ul><li><p>
Define AI architecture or agent design patterns (Principal AI Engineer)</p></li><li><p>
Build the agentic platform itself (external partner)</p></li><li><p>
Build the backend API or data pipelines</p></li><li><p>
Fine-tune or train models</p></li><li><p>
Define governance policies (Governance Lead)</p></li></ul><p><b>
Ideal Candidate</b></p><p>
Has shipped an LLM-based feature that non-technical users depend on daily &mdash; and has been responsible when it breaks. Knows the hardest part of applied AI is the fallback, the observability, and the human review loop &mdash; not the prompt. Can evaluate someone else's agent demo and quickly identify whether it's production-ready or held together with string. Works well under a senior AI lead &mdash; takes architectural direction and executes with high quality and speed. Not attached to a particular model &mdash; the job is reliable output.</p>
</div><div><ul><li><p>LLM APIs (Claude, GPT-4, open-weight models) &mdash; structured output, JSON mode, system prompts</p></li><li><p>
Prompt engineering for classification &mdash; zero-shot and few-shot</p></li><li><p>
Python &mdash; async API calls, retry logic, exponential backoff</p></li><li><p>
LLM evaluation &mdash; precision/recall, human-AI agreement scoring</p></li><li><p>
Structured output &mdash; JSON schema enforcement, Pydantic validation</p></li><li><p>
Open-weight / sovereign model APIs (Falcon, Llama, or equivalent)</p></li><li><p>
Token budgeting and context window management</p></li><li><p>
AI observability &mdash; output quality monitoring, anomaly detection</p></li><li><p>
FastAPI and Docker</p></li></ul>
</div></div></div><div><div><div><div><p>Location:
</p><div>Istanbul, Turkey
</div></div><div><p>Seniority:
</p><div>Senior
</div></div><div><p>Technologies:
</p><div>Python
</div></div></div></div><div><div><p>Benefits:
</p><ul><li><b>Paid Vacation
</b></li><li><b>Hybrid Work (home/office)
</b></li><li><b>Sick Days
</b></li><li><b>Sport/Insurance Compensation
</b></li><li><b>Holidays Day Off
</b></li><li><b>English Classes
</b></li><li><b>Training Compensation
</b></li><li><b>Transportation compensation</b></li></ul></div></div></div></div></div></div>

Blue Matter

<div><p>Blue Matter is a rapidly growing management consultancy focused on the biopharmaceutical industry. We partner with clients to help them achieve commercial success across the lifecycle of their products, portfolios, and organizations.</p><p>
Blue Matter is hiring for the <b>Technology and Operations Practice within the Blue Matter Insights Team</b>. Our Technology &amp; Operations practice integrates <b>strategy, data, and AI </b>to deliver scalable, technology-enabled solutions for BioPharma Clients. The practice has developed a <b>category-defining platform called Twine&trade;</b>&mdash; an AI-first customer intelligence platform purpose-built for life sciences that is already seeing strong traction in the market. The successful candidate will play a <b>direct role in shaping, enhancing, and scaling Twine </b>as a core capability within our portfolio.</p><p>
As an <b>AI Engineer / Senior AI Engineer</b>, you will work across Blue Matter as a technical expert in AI and emerging technologies, playing a key role in designing, building, and deploying AI-powered solutions for both internal teams and clients. You will be at the forefront of integrating APIs, building proof-of-concepts (POCs), developing internal applications, and creating client-facing tools and services. This role is ideal for someone who is passionate about staying current with the rapidly evolving AI landscape and eager to apply cutting-edge techniques to solve real-world problems in life sciences.</p><p><b>
Primary Responsibilities</b></p><p>
As a key member of cross-functional client-facing teams, develop and execute project plans to support initiatives that include, but are not limited to:</p><ul><li>
Design, develop, and deploy AI/ML solutions and Generative AI applications to support commercial data and analytics for life sciences companies</li><li>
Integrate third-party AI APIs (e.g., OpenAI, Anthropic, Google Gemini, Hugging Face) into internal and client-facing applications to deliver intelligent, scalable solutions</li><li>
Build proof-of-concepts (POCs) and rapid prototypes to demonstrate the value of AI-driven approaches for product launch analytics, drug discovery, patient analytics, and data operations</li><li>
Develop and maintain internal tools and applications that streamline workflows, automate operational processes, and enhance team productivity using AI</li><li>
Build client-facing tools and services that leverage AI to deliver actionable insights, interactive dashboards, and intelligent recommendations</li><li>
Utilize cloud-service providers (e.g., AWS, Azure, or GCP) including services such as S3, EC2, Lambda, Azure OpenAI, and SageMaker to build and deploy scalable AI solutions</li><li>
Collaborate with onshore/offshore teams to ensure timely, high-quality deliverables, providing technical guidance and code reviews</li><li>
Design and implement data pipelines and ETL processes to support AI model training, fine-tuning, and inference workflows</li><li>
Investigate and evaluate emerging AI frameworks, tools, and models to continuously improve the team&rsquo;s AI capabilities and stay ahead of industry developments</li><li>
Contribute to Blue Matter&rsquo;s Internal platform rollout and other AI-powered product initiatives in client environments</li><li>
Address client questions, gather requirements, and deliver AI-powered solutions aligned with business goals</li><li>
Ensure project success through effective collaboration and meeting deadlines using agile delivery model</li><li>
Stay current with the latest advancements in AI/ML, LLMs, prompt engineering, RAG, agents, and related technologies, and evangelize best practices across the organization</li></ul><p><b>
Desired Experience and Skills</b></p><ul><li>
Hands-on experience building AI/ML applications, including working with LLMs, Generative AI, and NLP frameworks</li><li>
Strong proficiency in Python and experience with AI/ML libraries and frameworks (e.g., LangChain, LlamaIndex, Hugging Face Transformers, PyTorch, TensorFlow)</li><li>
Proven experience integrating AI APIs (e.g., OpenAI, Anthropic, Google Vertex AI, Azure OpenAI) into production-grade applications</li><li>
Experience building and deploying internal tools and applications (e.g., using Streamlit, Gradio, Flask, FastAPI, or React)</li><li>
Ability to build client-facing tools and services that are reliable, user-friendly, and production-ready</li><li>
Expertise in building proof-of-concepts (POCs) and rapid prototypes to validate AI-driven solutions</li><li>
Experienced with cloud platforms (AWS, Azure, GCP) and services</li><li>
Proficient in API development (RESTful/GraphQL) and version control (e.g., Git)</li><li>
Expertise in SQL, Python, and databases</li><li>
Familiar with data pipeline tools (e.g., Airflow, dbt) and data warehouse technologies (e.g., Snowflake, Redshift, Databricks)</li><li>
Knowledge of prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning, and AI agent frameworks</li><li>
Strong knowledge of AI safety, responsible AI practices, and model evaluation techniques</li><li>
Knowledge of US pharma datasets and healthcare concepts is a plus</li><li>
Strong problem-solving, communication, and client-facing skills</li><li>
Proficient in Agile/Scrum and project management tools (e.g., JIRA, Smartsheet)</li><li>
Genuine passion and curiosity for ongoing AI development, with a demonstrated habit of learning and experimenting with new AI tools, models, and techniques</li><li>
Excellent communication and presentation skills (internal and client facing)</li></ul><p><b>
Preferred Qualifications</b></p><ul><li>
Advanced degree(s) (PhD, MD, MBA, or MS) in Computer Science, AI/ML, Data Science, or a related field</li><li>
4+ years of AI/ML engineering experience (AI Engineer) or 6+ years (Senior AI Engineer), including client-facing roles experience</li><li>
Demonstrated ability to integrate business and industry knowledge in developing creative AI solutions</li><li>
Active contributions to open-source AI projects, personal AI projects, or published research in AI/ML</li><li>
Passion for Generative AI and its applications in life sciences and healthcare</li></ul><p><b>
Compensation:</b></p><p>
Base salary range: $100,000 - $150,000 per year. Generous incentive compensation structure.<br>
<br>
Benefits Package:</p><ul><li>
401k - generous employer match with immediate vesting and financial planning resources</li><li>
Comprehensive medical, dental and vision coverage options effective day 1 of employment</li><li>
Flexible spending account (FSA) or Health Savings Account (HSA)</li><li>
Company paid insurances including Short- and Long-Term Disability and Life insurance as well as additional voluntary options</li><li>
Paid parental leave for all new parents</li><li>
Health &amp; Wellness Benefit (e.g., gym memberships; $1000 reimbursement annually)</li><li>
Employee Assistance Program</li><li>
Generous paid time off including vacation, sick days, floating and company holidays</li></ul><div>
Blue Matter is an equal opportunity employer and does not discriminate against otherwise qualified applicants on the basis of race, color, religion, ancestry, age (40 and over), sex (including gender identity, sexual orientation and pregnancy), national origin, disability, marital status, parental status, genetic information, political affiliation, protected veteran status, or any other characteristic protected by law.</div><p>
</p><p>YFln9UxC3u</p></div>

AI Engineer / Senior AI Engineer

MASTER-WORKS

<div><p>Master Works is seeking an AI Engineer with strong generative AI prototyping experience to rapidly turn ideas into working AI-powered demos, proofs of concept, and early-stage products. The role sits between research and production engineering &mdash; combining hands-on use of large language models, multimodal models, retrieval-augmented generation, and agent frameworks to validate feasibility, explore new capabilities, and de-risk feature delivery. The role is central to accelerating AI innovation across the data, analytics, and intelligence portfolio, with a particular emphasis on bilingual (Arabic/English) AI use cases.</p><ul><li>
Rapidly design and build generative-AI prototypes and proofs of concept &mdash; from initial idea to working demo &mdash; using modern LLM APIs, open-source models, and prototyping frameworks.</li><li>
Design and implement retrieval-augmented generation (RAG) pipelines, including document ingestion, chunking strategies, embedding generation, vector storage, and retrieval orchestration.</li><li>
Build agentic systems that use tool/function calling, planning, and multi-step reasoning to automate complex tasks; integrate AI agents with internal APIs, databases, and external services.</li><li>
Engineer prompts, system instructions, and structured-output schemas; iterate on prompt design through measurable evaluation rather than guesswork.</li><li>
Work with multimodal AI capabilities &mdash; text, vision, speech (STT/TTS), and document understanding &mdash; and combine them into coherent end-to-end AI pipelines.</li><li>
Build and run AI evaluation frameworks (offline benchmarks, LLM-as-judge, golden-set regression testing) to measure quality, accuracy, latency, and cost across model and prompt variants.</li><li>
Adapt and fine-tune open-source models (LoRA, QLoRA, full fine-tuning) when proprietary or domain-specific behaviour is required, especially for Arabic-language tasks.</li><li>
Apply responsible-AI practices &mdash; safety filtering, hallucination mitigation, prompt-injection defence, PII handling, and bias awareness &mdash; especially for sensitive or regulated environments.</li><li>
Hand off mature prototypes to production engineering teams, providing clear documentation, evaluation results, deployment notes, and migration guidance.</li><li>
Demo prototypes and AI capabilities to internal stakeholders and clients; explain technical concepts, limitations, and risks to non-technical audiences.</li><li>
Track and evaluate fast-moving developments in the generative-AI ecosystem (new models, tools, techniques, and providers) and recommend adoption where they materially improve outcomes.</li><li>
Collaborate cross-functionally with product, design, data, and software engineering teams to shape AI features end-to-end and ensure they deliver real user value.</li></ul><br>
<b>
Requirements</b><br>
<ul><li>
Bachelor&rsquo;s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related discipline.
</li><li>Postgraduate qualification (MSc/PhD) in Machine Learning, Artificial Intelligence, Natural Language Processing, or a related field is preferred.</li><li>
Recognised certifications in machine learning, deep learning, generative AI, or cloud AI platforms (AWS, Azure, GCP) are a strong plus.</li><li>
3&ndash;5+ years of AI/ML engineering experience, with at least 1&ndash;2 years hands-on building generative-AI applications using modern LLMs.</li><li>
Demonstrable portfolio of shipped prototypes or production AI features &mdash; ideally including RAG systems, AI agents, or multimodal pipelines.</li><li>
Hands-on experience with leading LLM APIs (e.g., Anthropic Claude, OpenAI GPT, Google Gemini) and open-source model serving (e.g., Hugging Face, vLLM, Ollama).</li><li>
Strong Python proficiency, including modern AI libraries such as PyTorch, Hugging Face Transformers, LangChain or LlamaIndex, and standard data tooling.</li><li>
Working experience with embedding models, vector databases (e.g., Pinecone, Weaviate, Qdrant, FAISS), and retrieval evaluation techniques.</li><li>
Familiarity with rapid-prototyping tools (Streamlit, Gradio, Jupyter), API integration, and lightweight web back-ends.</li><li>
Experience designing AI evaluation harnesses (offline benchmarks, LLM-as-judge, golden sets) and using observability/tracing tools for AI systems.</li></ul><ul><li>
Exposure to fine-tuning techniques (LoRA, QLoRA, instruction tuning) and to Arabic-language NLP is strongly preferred.</li><li>
Working knowledge of containerisation (Docker), version control (Git), and cloud AI platforms (AWS Bedrock, Azure OpenAI, GCP Vertex AI) is a strong plus.</li><li>
Excellent leadership and team development skills.</li><li>
Strong bias for action and a rapid-prototyping mindset; able to ship a working demo in days, not months.</li><li>
Comfortable working under uncertainty &mdash; rapidly forming hypotheses, running experiments, and iterating based on evidence.</li><li>
Strong analytical and problem-solving skills, with the discipline to evaluate AI behaviour quantitatively rather than relying on intuition alone.</li><li>
Excellent verbal and written communication, with the ability to demo AI capabilities and explain technical concepts, limitations, and risks to non-technical audiences.</li><li>
Curiosity and continuous-learning mindset; actively follows the rapidly evolving generative-AI landscape and brings new ideas back to the team.</li><li>
Collaborative team player; comfortable in cross-functional teams of researchers, software engineers, designers, and client business owners.</li><li>
Cultural awareness and sensitivity, particularly when designing AI experiences for Arabic-speaking users in regional contexts.</li></ul></div>

Treasury Cube

<div>
 About Treasury Cube
 <p>
  Treasury Cube is a modern Treasury Management System (TMS) helping corporate treasury teams take control of cash, payments, bank connectivity, in-house banking, and FX exposure. We're building a platform that turns the daily grind of treasury operations, bank statement processing, cash positioning, intercompany settlements, payment workflows into something faster, smarter, and decision ready.
 </p>
 <p>
  We're now embedding AI deeply into the platform to give treasurers the kind of insight, automation, and conversational access that legacy TMS vendors simply can't deliver. This is where you come in.
  <br/>
 </p>
 The Role
 <p>
  We're looking for an
  <strong>
   AI Engineer
  </strong>
  to lead the design and delivery of AI capabilities across Treasury Cube. You'll own the AI engineering stack end-to-end from model orchestration on
  <strong>
   Azure AI
  </strong>
  , to retrieval pipelines over treasury data, to the production systems that put insights and automation in front of CFOs and treasurers every day.
 </p>
 <p>
  <br/>
 </p>
 <p>
  This is a hands-on, build-from-the-ground-up role. You'll shape architecture, set patterns, and ship features customers actually use within weeks, not quarters.
  <br/>
 </p>
 What You'll Build
 <ul>
  <li>
   <strong>
    AI Insights Engine:
   </strong>
   Generate explanatory commentary and recommendations on cash positions, FX exposures, payment anomalies, forecast variances, and intercompany flows. Think: turning a 12-bank, 8-currency cash dashboard into a one-paragraph "here's what changed and why" briefing.
  </li>
  <li>
   <strong>
    AI Chatbox / Conversational Treasury Assistant:
   </strong>
   A natural-language interface where users can ask "what's my USD position next Friday?" or "show me payments held up in approval over $500K" and get accurate, grounded answers backed by the underlying treasury data.
  </li>
  <li>
   <strong>
    AI-Based Task &amp; Notification Automation:
   </strong>
   Intelligent routing, prioritization, and summarization of alerts (covenant breaches, sweep failures, payment exceptions, hedge effectiveness drift). Auto-generated tasks with context, suggested actions, and assignment logic.
  </li>
  <li>
   <strong>
    Monitoring &amp; Observability for AI Systems
   </strong>
   : Token usage, latency, hallucination detection, prompt regression, cost-per-customer tracking, and human-in-the-loop feedback pipelines. If we can't measure it, we won't trust it in front of treasurers.
   <br/>
  </li>
 </ul>
 What We're Looking For
 <p>
  <strong>
   Must-haves:
  </strong>
 </p>
 <ul>
  <li>
   3+ years of software engineering experience, with at least 1&ndash;2 years building production LLM or AI applications
  </li>
  <li>
   <strong>
    Strong, hands-on experience with the Azure AI stack
   </strong>
   Azure OpenAI Service, Azure AI Foundry, Azure AI Search (vector + hybrid retrieval), Azure AI Document Intelligence, and Azure Content Safety
  </li>
  <li>
   Solid grasp of
   <strong>
    RAG architectures
   </strong>
   , prompt engineering, function/tool calling, structured outputs, and evaluation frameworks
  </li>
  <li>
   Experience designing
   <strong>
    agentic workflows
   </strong>
   and multi-step automation (Azure Functions, Logic Apps, Durable Functions, or equivalent)
  </li>
  <li>
   Strong backend engineering fundamentals Python and/or C#/.NET, REST APIs, async patterns, SQL
  </li>
  <li>
   Production mindset: you think about cost, latency, observability, prompt versioning, and graceful degradation from day one
  </li>
 </ul>
 <p>
  <strong>
   Nice to have:
  </strong>
 </p>
 <ul>
  <li>
   Experience in FinTech, Treasury, ERP, or financial data domains
  </li>
  <li>
   Familiarity with ISO 20022 (CAMT, PAIN), SWIFT, or banking integrations
  </li>
  <li>
   Background in MLOps / LLMOps (Azure ML, Prompt Flow, model evaluation pipelines)
  </li>
  <li>
   Hands-on with Semantic Kernel, LangChain, or LlamaIndex
  </li>
  <li>
   Experience with multi-tenant SaaS architectures and data isolation patterns
  </li>
 </ul>
 <p>
  <br/>
 </p>
 Why Join Treasury Cube
 <ul>
  <li>
   <strong>
    Real ownership.
   </strong>
   You'll be the AI lead, not the third engineer on a team of fifteen. Architecture decisions you make this quarter will shape the platform for years.
  </li>
  <li>
   <strong>
    A domain that rewards intelligence.
   </strong>
   Treasury data is structured, high-stakes, and underserved by AI. The problems are genuinely hard, and the wins are visible to CFOs the day you ship.
  </li>
  <li>
   <strong>
    Fast feedback loops.
   </strong>
   Small customer base today (early-stage), which means you'll talk to actual users, see your features in production within weeks, and iterate based on real treasurer feedback.
  </li>
  <li>
   <strong>
    Work that compounds.
   </strong>
   Every insight, automation, and chatbox capability you ship becomes part of a platform that scales across customers not a one-off engagement.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 How to Apply
 <p>
  Send your CV, GitHub or portfolio (if relevant), and a short note on
  <strong>
   one production AI system you've built
  </strong>
  what it does, what you'd change, and why to
  <strong>
   HR@treasurycube.com
  </strong>
  .
 </p>
 <p>
  Bonus points if you include thoughts on how you'd approach building a treasury AI assistant that earns the trust of a CFO.
 </p>
</div>


Scotiabank

<p></p><div><p>Requisition ID: 257554<br>
<br>
Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.</p><p></p><div><b><br>
Purpose</b></div><br>
<div></div>
<div>
The AI Engineer is an individual contributor responsible for designing, building, and operationalizing enterprise grade AI solutions in a highly regulated banking environment. This role will execute AI engineering and MLOps/LLMOps, ensuring AI solutions are secure, scalable, auditable, and production ready. You will work with Senior Engineers to build and deploy production grade AI systems end to end.</div><br>
<div></div>
<div><b>
What You'll Do</b></div><br>
<div></div>
<div><b>
Production AI Engineering (Build, Deploy, Run)</b></div>
<ul>
<li>Implement production‑grade AI services and pipelines (batch and real‑time) with strong focus on reliability, performance, and operational excellence in the cloud</li>
<li>Execute, manage and support the packaging and deployment of models and solutions as scalable services (APIs, jobs, agents) with clear SLAs, monitoring, alerting, and runbooks.</li>
<li>Own complex problem resolution across environments, including production incidents related to AI systems.</li>
</ul>
<div><b>Governance‑by‑Design (Banking &amp; Regulatory Alignment)</b></div>
<ul>
<li>Embed <b>AI governance directly into engineering workflows</b>, including:
<ul><li>Security and access controls</li>
<li>Data classification and handling</li>
<li>Model risk management requirements</li>
<li>Privacy and consent controls</li>
<li>Responsible AI principles</li>
<li>Auditability and regulatory traceability</li>
</ul></li><li>Partner closely with Risk, Compliance, Legal, and Architecture teams to ensure AI solutions meet internal and external regulatory expectations.</li>
</ul>
<div><b>Generative AI &amp; Advanced AI Capabilities</b></div>
<ul>
<li>Implementation of <b>Generative AI</b> patterns such as Retrieval‑Augmented Generation (RAG), embeddings, semantic search, and agent workflows.</li>
<li>Ensure GenAI solutions are grounded in approved data sources, governed access, logging, and retention policies.</li>
<li>Define evaluation and monitoring approaches for GenAI outputs in regulated use cases.</li>
</ul>
<div><b>Enterprise MLOps / LLMOps
</b></div><ul><li>Implement <b>automated ML/LLM delivery pipelines</b> covering training, evaluation, approval, deployment, and rollback.</li>
<li>Implement standards for model versioning, reproducibility, environment isolation, and controlled releases.</li>
<li>Reduce time‑to‑production while increasing safety, repeatability, and governance through automation.</li></ul><br>
<div></div>
<div><b>
What You'll Bring</b></div><br>
<div></div>
<ul>
<li>Bachelor&rsquo;s degree in Computer Science, Engineering, or equivalent practical experience.</li>
<li>3+ years of experience in cloud engineering, with 3+ years focused on AI/ML systems</li>
<li>Expert‑level proficiency in Python, SQL and cloud infrastructure</li>
<li>Hands‑on experience deploying AI solutions in cloud environments (Azure and GCP).</li>
<li>Deep understanding of production concerns: reliability, scalability, observability, cost, and security.</li>
<li>Experience delivering AI solutions in regulated industries (banking, financial services, insurance, healthcare).</li>
<li>Strong familiarity with model risk management, audit requirements, and regulatory review processes.</li>
<li>Hands‑on experience with enterprise MLOps / LLMOps tooling and platform design.</li>
<li>Experience designing platform‑level AI capabilities, not just individual models.</li></ul><br>
<div></div>
<p><b>
Interested?</b></p><br>
<p></p>
<p>
If your experience is closely related but doesn&rsquo;t align perfectly with every qualification, we do encourage you to apply - you might be the right candidate for this or other roles at Scotiabank!</p>
<p>
At Scotiabank, every employee is empowered to reach their fullest potential, respected for who they are and, embraced for their differences. That&rsquo;s why we work to grow and diversify talent and engage employees in a performance-oriented culture.</p><p><br>
<b>
What's in it for you?</b></p><br>
<p></p>
<p>
Scotiabank wants you to be able to bring your best self to work &ndash; and life, every day. With a focus on holistic well-being, our many flexible benefit programs are designed to help support your unique family, financial, physical, mental, and social health needs.</p><br>
<p></p>
<p>
#Dallas</p><br>
<p></p>
<p>
Location(s): United States : Texas : Dallas<br>
<br>
Scotiabank is a leading bank in the Americas. Guided by our purpose: &quot;for every future&quot;, we help our customers, their families and their communities achieve success through a broad range of advice, products and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets.
<br>
<br>
At Scotiabank, we value the unique skills and experiences each individual brings to the Bank, and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including, but not limited to, an accessible interview site, alternate format documents, ASL Interpreter, or Assistive Technology) during the recruitment and selection process, please let our Recruitment team know. Candidates must apply directly online to be considered for this role. We thank all applicants for their interest in a career at Scotiabank; however, only those candidates who are selected for an interview will be contacted.</p></div>

Qureos

<p><strong>AI Engineer</strong><br /><br /><strong>Company:** Qureos</strong><br /><strong>Location:** Karachi, Pakistan</strong><br /><strong>Employment Type:** Full-Time</strong><br /><br /><strong>About Qureos</strong><br /><br /><strong>Qureos is a dynamic and forward-thinking organisation dedicated to shaping the future of career development. We are committed to empowering professionals by connecting them with meaningful opportunities, mentorship, and the skills needed to thrive in a competitive landscape. Our innovative platform leverages technology to bridge the gap between talent and industry, and we are now seeking a talented AI Engineer to join our pioneering team.</strong><br /><br /><strong>Job Overview</strong><br /><br /><strong>We are looking for a highly skilled and motivated AI Engineer to join our team in Karachi. The ideal candidate will be passionate about artificial intelligence and its application in solving real-world challenges. You will play a crucial role in designing, developing, and deploying cutting-edge AI and machine learning models that power our platform's core features, from intelligent job matching and skill analysis to personalised career guidance. This is an exciting opportunity to make a significant impact within a growing company at the forefront of the EdTech and recruitment industries.</strong><br /><br /><strong>Responsibilities</strong><br /><br /><strong>- Design, build, and deploy robust machine learning models to enhance our platform's capabilities.</strong><br /><strong>- Develop and refine recommendation systems to accurately match candidates with relevant job opportunities, projects, and mentors.</strong><br /><strong>- Utilise Natural Language Processing (NLP) techniques to analyse and interpret data from resumes, job descriptions, and user interactions.</strong><br /><strong>- Collaborate with cross-functional teams, including software engineers, product managers, and data scientists, to integrate AI solutions into our products.</strong><br /><strong>- Manage the end-to-end lifecycle of AI models, including data collection, preprocessing, training, evaluation, and deployment.</strong><br /><strong>- Stay current with the latest advancements in AI, machine learning, and deep learning to drive continuous innovation.</strong><br /><strong>- Ensure the ethical and responsible use of AI, maintaining fairness and avoiding bias in all models.</strong><br /><strong>- Optimise AI models for performance, scalability, and efficiency.</strong><br /><br /><strong>Qualifications</strong><br /><br /><strong>- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.</strong><br /><strong>- Proven commercial experience as an AI Engineer, Machine Learning Engineer, or in a similar role.</strong><br /><strong>- Strong programming proficiency in Python and extensive experience with key AI/ML libraries such as TensorFlow, PyTorch, and scikit-learn.</strong><br /><strong>- Solid understanding of machine learning algorithms, deep learning, NLP, and statistical modelling.</strong><br /><strong>- Experience with data processing and analysis using tools like Pandas and NumPy.</strong><br /><strong>- Familiarity with cloud computing platforms (e.g., AWS, GCP, Azure) and MLOps practices is highly desirable.</strong><br /><strong>- Excellent problem-solving abilities and a creative, analytical mindset.</strong><br /><strong>- Strong communication skills and the ability to work effectively in a collaborative team environment.</strong><br /><br /><strong>Benefits</strong><br /><br /><strong>- A competitive salary and comprehensive benefits package.</strong><br /><strong>- Opportunities for professional development and continuous learning.</strong><br /><strong>- A vibrant and supportive work culture that encourages innovation and collaboration.</strong><br /><strong>- The chance to work on meaningful projects that directly impact people's careers.</strong><br /><strong>- Flexible and modern working environment.</strong></p>

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