llm engineer

TAWANTECH

<div>Job Summary
We are seeking an AI Software Engineer with hands-on experience in Large Language Models (LLMs) and Generative AI technologies to develop intelligent solutions that enhance customer experience, operational efficiency, and knowledge management within the banking sector.
Key Responsibilities<ul><li>Design, develop, and deploy AI-powered applications leveraging LLMs and Generative AI.</li><li>Build Retrieval-Augmented Generation (RAG) solutions using enterprise banking data.</li><li>Develop intelligent assistants, chatbots, and knowledge management solutions.</li><li>Integrate AI services with banking applications and APIs.</li><li>Fine-tune, evaluate, and optimize LLM performance.</li><li>Ensure compliance with banking security, governance, and regulatory requirements.</li><li>Collaborate with business, data, and technology teams to deliver AI-driven solutions.</li></ul> 
Requirements<ul><li> 
4+ years of software engineering experience.</li><li>Strong Python programming skills.</li><li>Experience with LLMs, Generative AI, and Prompt Engineering.</li><li>Experience with LangChain, LlamaIndex, Vector Databases, and RAG architectures.</li><li>Familiarity with machine learning frameworks and AI deployment practices.</li><li>Experience with cloud platforms (AWS, Azure, GCP) is preferred.</li></ul>
Preferred Qualifications<ul><li>Banking or Financial Services experience.</li><li>Experience developing AI copilots or customer-facing AI solutions.</li><li>Knowledge of AI governance and responsible AI practices.</li></ul></div>

AI Software Engineer / LLM Engineer

Dizzaract FZ LLC

01. ABOUT THE COMPANYDizzaract is a product-driven company operating at the intersection of gaming, digital platforms, and AI. We build and scale multiple products &mdash; including Farcana 2.0, Gamed, and FAR Labs &mdash; each exploring a different space, but united by a shared approach: moving fast, staying curious, and focusing on things that people actually use. We operate as a collaborative, non-hierarchical team where ideas are valued based on their impact, not their origin, and where AI is embedded across everything we build &mdash; from infrastructure to product decisions.02. ABOUT THE ROLEFAR Labs is building FAR AI &mdash; a high-performance decentralized AI inference network that transforms the global computing landscape by turning consumer and enterprise GPUs into a unified AI engine. Following a successful alpha launch in internet cafes across the UAE, we are scaling infrastructure to onboard large-scale gaming cafe fleets and top-tier data centers worldwide.We&rsquo;re looking for an AI Automation Engineer (Agent Systems Builder) who can design and deploy multi-agent systems that operate across the company &mdash; from internal workflows to product-level automation.This is a hands-on role focused on building structured, reliable AI systems. You will turn LLMs into coordinated agents that can reason, take action, and operate within defined environments.03. WHO YOU ARE<ul><li>LLM Engineer</li><li>You understand how language models actually work &mdash; tokenisation, logits, prompting, and system behaviour.</li><li>Agent Systems Builder</li></ul>You&rsquo;ve worked with or built multi-agent systems and understand orchestration, roles, and control flows.<ul><li>Automation Thinker</li></ul>You design systems that take action &mdash; not just generate outputs.You can wrap agents into APIs, manage integrations, and handle async workflows.You understand access control, permissions, and how to prevent unsafe behaviour in agent systems.04. RESPONSIBILITIES<ul><li>Backend Engineer</li><li>Security-Aware</li><li>Prompt Engineering &amp; System Design</li><li>Design structured, multi-layered prompt systems with context management, self-correction, and reliability controls.</li><li>AI Hub Development</li></ul>Build and configure internal AI systems (Claude, GPT, open-source models) connected to company data, tools, and APIs.<ul><li>Multi-Agent Architecture</li></ul>Design and implement hierarchical agent systems (orchestrators, specialised agents, execution layers) with clear responsibilities and control flows.Integrate AI agents with internal systems &mdash; CRM, databases, messaging platforms, CI/CD &mdash; enabling real actions and workflows.Build and maintain tools that allow agents to interact with external systems (APIs, services, internal infrastructure).05. REQUIREMENTS<ul><li>Integration &amp; Automation</li><li>Tooling &amp; Execution</li><li>Strong understanding of LLM internals (tokenisation, logits, temperature, system prompts, RAG).</li><li>Experience building or working with agent frameworks (LangChain, LangGraph, AutoGen, CrewAI, or custom systems).</li><li>Strong backend experience (Python or TypeScript).</li><li>Experience building APIs, handling async workflows, and integrating external systems.</li><li>Experience implementing function calling, tools, and agent execution workflows.</li><li>Understanding of prompt security, injection risks, and hallucination control.</li><li>Experience integrating AI systems into real business processes or products.</li></ul>06. NICE TO HAVE<ul><li>Experience building multi-agent systems in production.</li><li>Experience integrating agents with CRM, databases, or CI/CD pipelines.</li><li>Experience with Model Context Protocol (MCP) or similar tooling approaches.</li><li>Experience working in fast-paced or AI-first environments.</li></ul>07. WHAT WE OFFER<ul><li>Real ownership and direct impact on a global AI infrastructure product</li><li>Fast execution, low bureaucracy, and a highly collaborative, idea-driven team</li><li>Exposure to cutting-edge AI, distributed systems, and hardware-level optimization</li><li>Competitive salary with performance-based incentives</li><li>24 days annual leave, plus public holidays</li><li>Health insurance</li><li>Modern office in Yas Creative Hub</li><li>Continuous learning through real-world problem solving &mdash; not just theory</li><li>The opportunity to shape what you're working on and influence product direction</li><li>A diverse, open-minded team where ideas are genuinely heard</li></ul>08. HOW TO APPLYAs part of your application, please share:<ul><li>A diagram or description of a multi-agent system you've built (or would build).</li><li>An example of a complex prompt using tools/function calling.</li><li>Your approach to enforcing access control across agent systems.</li></ul>Application Question(s):<ul><li>Do you understand tokenization, logits, temperature, and their effect on LLM output behavior?</li><li>Have you ever implemented a Retrieval-Augmented Generation (RAG) pipeline from scratch (not using a managed service)?</li><li>On a scale of 1&ndash;5, how confident are you in building prompts resistant to injection attacks? (1 = not confident, 5 = expert)</li><li>Rate your understanding of API authentication methods (OAuth, API keys, JWT)? (1 = vague, 5 = expert)</li><li>Have you built a self-correcting prompt with an automated feedback loop?</li><li>Number of years of professional experience in LLM engineering (0, 1, 2, 3, 4, 5+).</li><li>Is your portfolio submission complete with all three requested items (diagram, complex prompt, access control thoughts)?</li><li>Have you built or deployed a multi-agent system (not just single-agent chatbots)?</li><li>Have you implemented function calling (tools) where an agent executes external APIs autonomously?</li><li>Number of production LLM automation projects you have shipped? (0, 1&ndash;2, 3&ndash;5, 6-8, 10+)</li><li>Have you integrated LLM agents with CRM, database, or CI/CD pipelines?</li><li>Can you build an API wrapper around an agent that handles async webhooks?</li><li>Rate your Python proficiency (1 = basic, 5 = architect-level)</li><li>Rate your TypeScript proficiency (1 = basic, 5 = architect-level)</li></ul>Work Location: In person

AI Automation Engineer / Architect of Agentic AI (Automator)

Experian

<div><div>Role Overview</div> 
We are looking for an enthusiastic Senior GenAI Engineer to drive our generative AI initiatives, with a strong focus on building GenAI utilities that elevate Quality Assurance practices and accelerate developer productivity across the engineering organisation.
You will design, build, and operationalise LLM-powered tools, agents, and platforms on AWS that are secure, scalable, and production-ready in a regulated environment.
Key Responsibilities<ul><li>
You will be #LI-hybrid based in Hyderabad and reporting to Director Engineering</li><li>
Work as a senior GenAI engineer in an agile team to deliver high-quality, production-grade GenAI solutions within agreed timelines, aligned with business requirements and agile principles.</li><li>
Design and build GenAI-powered utilities for QA - including intelligent test case generation, test data synthesis, automated defect triage, log/anomaly summarization, and assistive code/test review tooling - to measurably improve quality and engineering velocity.</li><li>
Develop reusable GenAI accelerators, libraries, and internal copilots that increase productivity across software engineering, QA, and operations teams.</li><li>
Translate business and engineering requirements into clean, scalable Python code, applying design patterns, secure coding practices, and modern LLM application architectures (RAG, tool-use, agents).</li><li>
Build and operate LLM applications on AWS using services such as Amazon Bedrock, SageMaker, Lambda, ECS/EKS, API Gateway, S3, DynamoDB, and OpenSearch.</li><li>
Establish patterns and frameworks for prompt engineering, retrieval-augmented generation, evaluation, observability, and continuous improvement of GenAI systems.</li><li>
Champion responsible AI - guardrails, PII protection, model governance, hallucination mitigation, and compliance with security and regulatory requirements.</li><li>
Collaborate closely with cross-functional team members - QA engineers, software engineers, architects, product, and security - to design, develop, test, and release GenAI-powered software.</li><li>
Contribute to development processes and practices, fostering a culture of continuous integration, delivery, evaluation, and improvement for GenAI systems.</li><li>
Provide clear and concise documentation for prompts, models, datasets, code, processes, and system architecture to support knowledge sharing and maintainability.</li></ul> 
 
<div>Experience and Skills</div> 
<ul><li>Bachelor's degree in engineering or a related discipline.</li><li>
3+ years of hands-on experience in software development, with at least 1+ years working on production GenAI / LLM-based applications.</li><li>
Proven experience building secure, high-volume, mission-critical systems in regulated industries (finance / insurance / healthcare).</li><li>
Expertise in translating business requirements into clean, scalable code using design patterns and security best practices.</li><li>
Strong individual contributor and effective team collaborator, comfortable working with QA, engineering, and product stakeholders.</li></ul>
Technical Skills<ul><li>
Strong Python development skills - building production services with frameworks such as FastAPI / Flask, Pydantic, asyncio, and modern packaging/testing tooling.</li><li>
Hands-on expertise with AWS for designing, deploying, and operating applications - Bedrock, SageMaker, Lambda, ECS/EKS, API Gateway, S3, DynamoDB, OpenSearch, IAM, CloudWatch, Step Functions.</li><li>
Strong working knowledge of GenAI fundamentals - LLMs (Claude, GPT, Llama, Mistral and similar), embeddings, tokenisation, context windows, model selection, and cost/performance trade-offs.</li><li>
Experience designing and shipping Retrieval-Augmented Generation (RAG) solutions using vector stores (OpenSearch, pgvector, Pinecone, FAISS) and hybrid retrieval patterns.</li><li>
Practical experience with prompt engineering, structured outputs, function/tool calling, and agentic patterns; familiarity with frameworks such as LangChain, LlamaIndex, Bedrock Agents, or similar.</li><li>
Experience defining and running LLM evaluation frameworks - golden datasets, automated evals, regression testing, and online metrics for quality, safety, and cost.</li><li>
Building and consuming REST / gRPC APIs using microservices / SOA, with strong API design, versioning, and security practices.</li><li>
Experience writing unit and integration tests (pytest), applying TDD, and building eval harnesses for LLM-based components.</li><li>
Expertise in creating, maintaining, and reusing frameworks / libraries for GenAI utilities and shared platform capabilities.</li><li>
Experience with model fine-tuning and parameter-efficient techniques (LoRA, QLoRA), distillation, and small-language-model deployment.</li><li>
Experience with agentic frameworks, multi-agent orchestration, and emerging standards such as Model Context Protocol (MCP).</li><li>
Clean code, clean architecture, SOLID principles, and design patterns applied to data- and ML-intensive systems.</li><li>
Expertise with SQL / NoSQL databases (PostgreSQL, DynamoDB, MongoDB) for application and feature/metadata storage.</li><li>
Proficient with Docker, Kubernetes, and Git (Bitbucket, GitHub, GitLab) and CI/CD practices.</li><li>
Experience working in Agile teams (Scrum or Kanban).</li><li>
Familiar with static code analysis, dependency scanning, and vulnerability management for AI/ML applications.</li></ul>
Desirable Skills<ul><li>
Demonstrable experience building GenAI utilities or copilots for QA and/or developer productivity (test generation, defect triage, code/test review assistants, documentation generation).</li><li>
AWS certifications - especially AWS Certified Machine Learning - Specialty, AI Practitioner, Solutions Architect, or Developer Associate.</li><li>
Experience deploying and scaling services on Amazon ECS / EKS and serverless GenAI architectures.</li><li>
Working knowledge of GenAI tools for coding (e.g., GitHub Copilot, Amazon Q Developer, Claude Code).</li><li>
Familiarity with Domain-Driven Design (DDD) and Event-Driven Architecture (EDA).</li><li>
Event streaming / messaging tools (Kafka, EventBridge, Kinesis, RabbitMQ, ActiveMQ).</li><li>
Proficient in Infrastructure as Code (IaC) using Terraform, CloudFormation, or AWS CDK.</li><li>
CI/CD tools such as GitHub Actions, GitLab CI, or Jenkins, with experience building MLOps / LLMOps pipelines.</li><li>
Experience with LLM observability and evaluation tooling (LangSmith, LangFuse, RAGAS, Helicone, Arize) and responsible-AI guardrails.</li></ul> 

 
<div>About Experian</div> 
Experian is a global data and technology company, powering opportunities for people and businesses around the world. We help to redefine lending practices, uncover and prevent fraud, simplify healthcare, create marketing solutions, and gain deeper insights into the automotive market, all using our unique combination of data, analytics and software. We also assist millions of people to realize their financial goals and help them save time and money.
We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more industry segments.
We invest in people and new advanced technologies to unlock the power of data. As a FTSE 100 Index company listed on the London Stock Exchange (EXPN), we have a team of 22,500 people across 32 countries. Our corporate headquarters are in Dublin, Ireland. Learn more at experianplc.com. 
Our uniqueness is that we celebrate yours. Experian's culture and people are important differentiators. We take our people agenda very seriously and focus on what matters; DEI, work/life balance, development, authenticity, collaboration, wellness, reward &amp; recognition, volunteering... the list goes on.
Experian's people first approach is award-winning; World's Best Workplaces&trade; 2024 (Fortune Global Top 25), Great Place To Work&trade; in 24 countries, and Glassdoor Best Places to Work 2024 to name a few. Check out Experian Life on social or our Careers Site and Glassdoor to understand why.
Experian is proud to be an Equal Opportunity and Affirmative Action employer. Innovation is a critical part of Experian's DNA and practices, and our diverse workforce drives our success. Everyone can succeed at Experian and bring their whole self to work, irrespective of their gender, ethnicity, religion, color, sexuality, physical ability or age. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.
Benefits
Experian care for employee's work life balance, health, safety and wellbeing. In support of this endeavor, we offer best-in-class family well-being benefits, enhanced medical benefits and paid time off.
Experian Careers - Creating a better tomorrow together
This is a hybrid remote/in-office role.<div></div>
Experian Careers - Creating a better tomorrow together</div>

Gen AI Engineer

Apple

The Intelligence Platform team empowers clients across Apple&rsquo;s operating systems with high-quality, user-centric knowledge and inferences that enable next-generation user experiences. We&rsquo;re an applied Machine Learning team that leverages state-of-the-art technologies-Generative AI, Large Language Models, RAG based systems, and emerging agentic AI patterns-to deliver high-quality inferences at scale! 
 
Description 
 
We are in search of a driven Machine Learning Engineer who has a strong understanding of LLMs and Generative AI and is excited to explore the rapidly evolving landscape of LLM-powered agents, tool-use models, and advanced reasoning techniques. You will help translate groundbreaking research into production systems, influence our technical direction, and shape the future of AI at Apple. 
 
Agentic AI is an emerging area for our team-prior experience is a plus but not required. What matters most is curiosity, strong ML fundamentals, and the ability to navigate and apply cutting-edge research!&quot;,&quot;responsibilities&quot;:&quot;Leading exploration and application of LLMs, Generative AI, and emerging agentic AI techniques (e.g., tool-use models, model-driven planning, task decomposition). 
 
Translating the latest research into high-performing, production-ready ML systems that enhance user experiences across Apple products. 
 
Helping define the technical direction for LLM-driven and agentic AI capabilities within the Intelligence Platform. 
 
Contributing to all phases of model development-problem formulation, experimentation, evaluation, fine-tuning, deployment, and continuous improvement. 
 
Collaborating cross-functionally to design and implement ML solutions, ensuring alignment with product goals, privacy requirements, and performance metrics. 
 
Driving innovation by identifying opportunities to incorporate advanced reasoning, grounding, or agentic patterns into existing ML systems. 
 
Preferred Qualifications 
 
Familiarity with agentic AI, structured tool-use models, or multi-turn reasoning systems. (Not required-experience is a plus, and interest in the domain is highly valued.) 
 
Hands-on experience with end-to-end LLM development: dataset curation, fine-tuning, evaluation, prompt optimization, or model hosting. 
 
Published research demonstrating contributions to LLMs, Generative AI, or related subfields. 
 
Ability to guide technical direction, mentor others, and collaborate effectively in a fast-paced environment. 
 
Minimum Qualifications 
 
Advanced degree (Master&rsquo;s or Ph.D.) in Computer Science, Machine Learning, Artificial Intelligence, or a related field. 
 
Strong experience in Machine Learning, with emphasis on LLMs, Generative AI, or large-scale deep learning systems. 
 
Demonstrated ability to read, interpret, and apply cutting-edge research to real-world engineering problems. 
 
Experience with model training, fine-tuning, or building scalable ML systems. 
 
Strong programming and problem-solving skills. 
 
Pay &amp; Benefits 
 
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $181,100 and $318,400, and your base pay will depend on your skills, qualifications, experience, and location. 
 
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits 
 
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Machine Learning Engineer, Proactive - Large Language Models, Generative AI & Agentic Systems

Ruby Labs

<div><h1 class="jobSectionHeader">About us</h1>
Ruby Labs is a leading tech company that creates and operates innovative consumer products. We offer a diverse range of opportunities across the health, education, and entertainment industries. Our innovative teams are driving the future of consumer-led products, and we're always looking for passionate individuals to join us. Learn more about our story at: https://rubylabs.com/about-us/<h1 class="jobSectionHeader">
About the role</h1>
At Ruby Labs we are looking for a Senior AI Engineer to own and drive the quality, reliability, and evolution of our AI systems in production.
This is a high-ownership role. You will be responsible for end-to-end delivery of major AI features, production stability of AI systems, and data-driven experimentation using tools like Langfuse, Mixpanel and OpenRouter. You&rsquo;ll work in a modern stack built on Next.js, TypeScript, Node.js, and Redis, collaborating closely with product, growth, data, and billing teams. Increasingly, this includes building agentic, tool-using AI systems &mdash; defining clean tool contracts (including MCP-based tools) and orchestrating how AI interacts with internal services and business systems.
Our engineering organization uses a squad-based structure. You will operate within an AI engineering squad, contributing as a senior technical voice and driving engineering quality within your area of the product.<h1 class="jobSectionHeader">
Key Responsibilities</h1><ul><li>
Take complete ownership and deliver major AI engineering features within agreed timelines.</li><li>
Own AI output quality, structure, and predictability across all user-facing AI interactions.</li><li>
Design, implement, and maintain output-type-based AI systems, including segmentation, routing, and enforcement.</li><li>
Ensure consistent output structure and formatting across different LLMs for the same request type.</li><li>
Integrate and orchestrate multiple LLM providers via OpenRouter, managing model selection, fallback strategies, and cost optimisations.</li><li>
Design and orchestrate tool-using and agentic AI workflows, defining clean tool contracts (including MCP-based tools), function-calling interfaces, and reliable AI-to-system integrations.</li><li>
Build and maintain complex, multi-step LLM workflows, including with orchestration frameworks such as LangChain or LlamaIndex, for advanced reasoning, context reuse, and retrieval.</li><li>
Design and manage production prompt systems with dynamic prompting, context injection, and conditional logic.</li><li>
Own the deployment and release of LLM experiments, prompt management, and Langfuse-based evaluation pipelines.</li><li>
Run A/B tests across models, analyse results, and present data-driven impact assessments of AI features and experiments.</li><li>
Monitor AI system metrics, quality signals, latency, and release health using Langfuse and other observability tools.</li><li>
Deep-debug complex LLM chains using Langfuse traces, identifying bottlenecks and optimising for cost, latency, and context-window usage, and build output-scoring systems to root-cause hallucinations and logic errors.</li><li>
Write clean, scalable, and maintainable TypeScript code across the Next.js and Node.js stack.</li><li>
Build reliable backend logic for AI systems, with strong error handling, request validation, fallback flows, and predictable behaviour in production, including reliable tool execution and AI-to-service integrations.</li><li>
Ensure high code quality through testing, code reviews, and clear engineering standards.</li><li>
Monitor, troubleshoot, and improve production performance, reliability, and system health.</li><li>
Drive maintainability and technical quality through solid architecture, refactoring, and disciplined release practices.</li></ul><h1 class="jobSectionHeader">
Qualifications</h1><ul><li>
6+ years of backend/full-stack software engineering experience, including production-grade TypeScript/Node.js. Experience with Next.js and/or Python is a plus.</li><li>
2+ years of experience building AI/LLM systems in production. Less experience may be considered for exceptional candidates.</li><li>
Deep hands-on experience working with LLM APIs (OpenAI, Anthropic, or similar) in production environments.</li><li>
Experience with Agentic AI, multi-agent orchestration, tool-based workflows (function calling/tool execution), and/or RAG pipelines, including indexing, retrieval, and re-ranking.</li><li>
Experience with LLM observability tools such as Langfuse, LangSmith, or similar platforms.</li><li>
Experience with AI gateways and model routing solutions, such as OpenRouter or equivalent technologies.</li><li>
Solid understanding of Redis and relational databases, such as PostgreSQL.</li><li>
Exceptional ownership mindset and personal responsibility for engineering quality and delivery.</li></ul><h1 class="jobSectionHeader">
Nice to have</h1><ul><li>
Experience with AI-centered development tools such as Cursor, Claude Code, Windsurf, or similar platforms.</li><li>
Familiarity with evaluation frameworks, including LLM-as-a-judge, RAGAS, or similar approaches.</li><li>
Experience working in high-pressure startup environments with rapid product iteration cycles.</li><li>
Experience with MCP (Model Context Protocol), including building MCP servers/clients or designing tool contracts for AI agents.</li><li>
Experience with edge and serverless runtimes, such as Cloudflare Workers, and supporting services including KV, Durable Objects, Queues, R2, and D1.</li><li>
Experience with payments, billing and checkout flows, or orchestration platforms.</li><li>
Practical experience fine-tuning models for domain-specific tasks or achieving strict JSON/schema compliance.</li><li>
Working proficiency in Python for data science, evaluation scripts, or AI tooling.</li></ul><h1 class="jobSectionHeader">
Location</h1>
Ruby Labs operates within the CET (Central European Time) zone. Applicants from any country are welcome to apply for the position as long as they are located within approximately &plusmn; 4 hours of CET. This ensures optimal collaboration and communication during working hours.<h1 class="jobSectionHeader">
Benefits</h1>
Discover the perks of being part of our vibrant team! We offer:<ul><li>
Remote Work Environment: Embrace the freedom to work from anywhere, anytime, promoting a healthy work-life balance.
</li><li>Unlimited PTO: Enjoy unlimited paid time off to recharge and prioritize your well-being, without counting days.
</li><li>Paid National Holidays: Celebrate and relax on national holidays with paid time off to unwind and recharge.
</li><li>Company-provided MacBook: Experience seamless productivity with top-notch Apple MacBooks provided to all employees who need them.
</li><li>Flexible Independent Contractor Agreement: Unlock the benefits of flexibility, autonomy, and entrepreneurial opportunities. Benefit from tax advantages, networking opportunities, reduced employment obligations, and the freedom to work from anywhere. Read more about it here: https://docs.google.com/document/d/1tzxGX4Uu7Ts_HCLFXESKLnKaaBfVCPf1f9AYZPrkjJM/preview?tab=t.0</li></ul>
Be part of our fast-growing team and seize this excellent opportunity for personal and professional growth!<h1 class="jobSectionHeader">
Interview Process</h1>
After submitting your application, we conduct a thorough review which typically takes 3 to 5 days, but may occasionally take longer due to the volume of applications received. If we see a potential fit, we proceed with the following steps:<ul><li>
Recruiter Screening (40 minutes)</li><li>
Technical Interview (60 minutes)</li><li>
Second Interview (30 minutes)</li><li>
Final Interview (20 minutes)</li></ul><h1 class="jobSectionHeader">
Life at Ruby Labs</h1>
At Ruby Labs, we move fast, aim high, and expect the same from our team. We&rsquo;re not here to play small&mdash;we&rsquo;re here to build, grow, and win. That means we look for people who are ambitious, driven, and ready to give their best every single day.
This is a place for individuals who thrive under pressure, embrace challenges, and see opportunity in every obstacle. If you&rsquo;re hungry to achieve, motivated by impact, and want to grow at the speed of your own ambition, Ruby Labs offers the platform to make it happen.
Here, effort is matched with reward. We recognize those who go all in and deliver results, and we create space for people who want more&mdash;more responsibility, more growth, and more success.
#LI-Remote</div>

Senior AI Software Engineer

Aegistech

<div>
 
 
 Responsibilities:
 
 
 <ul>
 <li>
 
 Opportunity hunting and workflow redesign
 
 &ndash; Lead Lean/Six Sigma discovery workshops; map value streams, assess process and data maturity, and log low-effort/high-impact AI use cases.
 </li>
 <li>
 
 Process and data maturity assessment
 
 &ndash; Evaluate each jobsite&rsquo;s current workflows and underlying data; surface gaps that block AI adoption and develop phased improvement plans with Operations Excellence to establish the right process baseline before deploying agents.
 </li>
 <li>
 
 Assess the market solutions
 
 &ndash; Evaluate off-the-shelf and platform tools; launch pilots, measure impact, and scale wins.
 </li>
 <li>
 
 Rapid AI-agent builds
 
 &ndash; Convert user stories into production-ready agents in Copilot Studio / Power Apps/Automate, ChatGPT Enterprise, or code-first frameworks within days; wire them to Teams/SharePoint on the front end and Databricks Lakehouse or other sources on the back end.
 </li>
 <li>
 
 Enterprise-grade engineering &amp; LLMOps
 
 &ndash; Build RAG pipelines backed by Delta tables, Unity Catalog, and Databricks Vector Search; automate infra with GitHub Actions / Posit; monitor latency, cost, adoption, and drift.
 </li>
 <li>
 
 Data integrations
 
 &ndash; Partner with Data Engineering to design and maintain ETL pipelines, API integrations, and event-driven connectors feeding RAG and agents.
 </li>
 <li>
 
 Cross-cloud orchestration
 
 &ndash; Blend OpenAI, Azure OpenAI, and AWS Bedrock behind secure custom connectors; package agents for seamless rollout.
 </li>
 <li>
 
 Change enablement
 
 &ndash; Train crews, gather feedback, iterate, and track adoption and ROI metrics; apply influence model principles to embed agents into daily routines and SOPs, and track behavior change KPIs.
 </li>
 <li>
 
 Stakeholder communication
 
 &ndash; Brief project leadership and clients on agent impact in clear business terms; contribute use cases and playbooks for &ldquo;Construction Site of the Future.&rdquo;
 </li>
 <li>
 
 Escalation &amp; hand-off
 
 &ndash; Draft clear user stories, data specs, and acceptance criteria for any complex solution that requires the central AI Solution Engineers or Data Engineering / Data Science team to lean in.
 </li>
 </ul>
 
 
 
 
 
 Qualifications:
 
 
 <ul>
 <li>
 4+ years in AI engineering / full-stack data applications or data science, including 2+ years building production LLM/RAG solutions.
 </li>
 <li>
 Bachelor&rsquo;s in CS, Engineering, Physics, or a related field; Master&rsquo;s preferred.
 </li>
 <li>
 Prior hands-on work in construction or heavy process industries (manufacturing, oil &amp; gas, chemicals) is a significant plus.
 </li>
 <li>
 Demonstrated process excellence background (Lean/Six Sigma Green Belt or equivalent) with experience diagnosing process and data gaps and supporting change management plans with Operations Excellence.
 </li>
 <li>
 Strong facilitation and communication skills.
 </li>
 <li>
 Hands-on expertise with Copilot Studio, Power Apps/Automate, custom connectors, and CoE Toolkit governance.
 </li>
 <li>
 Programming &amp; data stack: Python, SQL, Databricks Lakehouse, vector stores.
 </li>
 <li>
 DevOps &amp; IaC: GitHub Actions (or Azure DevOps) and Posit Workbench/Connect automation or comparable CI/CD tooling; strong Git/GitHub workflow discipline.
 </li>
 <li>
 Integration &amp; ETL skills: Foundational understanding of ETL/ELT design, Airflow or Databricks Workflows, and REST/GraphQL API development; proven collaboration with Data Engineering on source-to-lake and lake-to-agent pipelines.
 </li>
 <li>
 Willing and able to travel and work on active job sites.
 </li>
 </ul>
 
 
</div>

Site AI Engineer

Techster IT Services

Job Description:Multiple roles in different locations: New Haven, vegas, Dallas, Temple Texas, Fort Meyers, Boston Join project teams across the U.S. as the on-site catalyst who turns AI ideas into working reality. Partnering with each project&rsquo;s AI Champion (Project Manager or Superintendent), you&rsquo;ll uncover pain points, redesign workflows, and deploy AI agents that cut down reporting, accelerate RFIs, simplify lookahead planning, progress updates, materials tracking, and more. When needed, you will develop user stories and coordinate development with the central AI Studio. You&rsquo;ll help advance the vision of the &ldquo;Construction Site of the Future,&rdquo; showing how agentic AI will transform project operations.Responsibilities<ul><li>Opportunity hunting and workflow redesign &ndash; Lead Lean/Six Sigma discovery workshops; map value streams, assess process and data maturity, and log low-effort/high-impact AI use cases.</li><li>Process and data maturity assessment &ndash; Evaluate each jobsite&rsquo;s current workflows and underlying data; surface gaps that block AI adoption and develop phased improvement plans with Operations Excellence to establish the right process baseline before deploying agents.</li><li>Assess the market solutions &ndash; Evaluate off-the-shelf and platform tools; launch pilots, measure impact, and scale wins.</li><li>Rapid AI-agent builds &ndash; Convert user stories into production-ready agents in Copilot Studio / Power Apps/Automate, ChatGPT Enterprise, or code-first frameworks within days; wire them to Teams/SharePoint on the front end and Databricks Lakehouse or other sources on the back end.</li><li>Enterprise-grade engineering &amp; LLMOps &ndash; Build RAG pipelines backed by Delta tables, Unity Catalog, and Databricks Vector Search; automate infra with GitHub Actions / Posit; monitor latency, cost, adoption, and drift.</li><li>Data integrations &ndash; Partner with Data Engineering to design and maintain ETL pipelines, API integrations, and event-driven connectors feeding RAG and agents.</li><li>Cross-cloud orchestration &ndash; Blend OpenAI, Azure OpenAI, and AWS Bedrock behind secure custom connectors; package agents for seamless rollout.</li><li>Change enablement &ndash; Train crews, gather feedback, iterate, and track adoption and ROI metrics; apply influence model principles to embed agents into daily routines and SOPs, and track behavior change KPIs.</li><li>Stakeholder communication &ndash; Brief project leadership and clients on agent impact in clear business terms; contribute use cases and playbooks for &ldquo;Construction Site of the Future.&rdquo;</li><li>Escalation &amp; hand-off &ndash; Draft clear user stories, data specs, and acceptance criteria for any complex solution that requires the central AI Solution Engineers or Data Engineering / Data Science team to lean in.</li></ul>Qualifications<ul><li>4&ndash;6+ years in AI engineering / full-stack data applications or data science, including 2+ years building production LLM/RAG solutions.</li><li>Bachelor&rsquo;s in CS, Engineering, Physics, or a related field; Master&rsquo;s preferred.</li><li>Prior hands-on work in construction or heavy process industries (manufacturing, oil &amp; gas, chemicals) is a significant plus.</li><li>Demonstrated process excellence background (Lean/Six Sigma Green Belt or equivalent) with experience diagnosing process and data gaps and supporting change management plans with Operations Excellence.</li><li>Strong facilitation and communication skills.</li><li>Hands-on expertise with Copilot Studio, Power Apps/Automate, custom connectors, and CoE Toolkit governance.</li><li>Programming &amp; data stack: Python, SQL, Databricks Lakehouse, vector stores.</li><li>DevOps &amp; IaC: GitHub Actions (or Azure DevOps) and Posit Workbench/Connect automation or comparable CI/CD tooling; strong Git/GitHub workflow discipline.</li><li>Integration &amp; ETL skills: Foundational understanding of ETL/ELT design, Airflow or Databricks Workflows, and REST/GraphQL API development; proven collaboration with Data Engineering on source-to-lake and lake-to-agent pipelines.</li><li>Willing and able to travel and work on active jobsites.</li></ul>Pay: $80.00 - $90.00 per hourWork Location: In person

Search 1636+ Llm engineer jobs near you this June. New roles added regularly across leading industries. Apply today.

1636 llm engineer jobs Near Me

AI Software Engineer / LLM Engineer

AI Automation Engineer / Architect of Agentic AI (Automator)

Gen AI Engineer

Machine Learning Engineer, Proactive - Large Language Models, Generative AI & Agentic Systems

Senior AI Software Engineer

Site AI Engineer

Site AI Engineer