engineering agent

Texas Instruments

<p></p><div><div><div><div><div><div><div><div><div><div><div><div><b>Change the world. Love your job.</b></div><p>
Texas Instruments is transforming semiconductor manufacturing through AI. This role sits within the Smart Manufacturing Automation (SMA). organization You will design, build, and operate the agentic AI infrastructure that powers factory intelligence across TI's global fab and assembly/test operations to connect large language models to live manufacturing data systems and enabling rapid decision support at scale.</p><p><b>
What You'll Do</b></p><ul><li><div>
Architect and deliver multi-agent AI systems using the A2A (Agent-to-Agent) protocol, including orchestrator and sub-agent topologies that span multiple factory data sources.</div></li><li><div>
Build and maintain MCP (Model Context Protocol) servers in Python, containerized with Docker and deployed on VMs and Kubernetes clusters; register agents and MCP servers.</div></li><li><div>
Integrate LLM inference into factory workflows; manage LiteLLM project credentials, budgets, and routing configurations.</div></li><li><div>
Develop agent tools that query manufacturing databases (MES, FDC, APC, OEE, etc.) and expose clean interfaces for downstream AI consumption.</div></li><li><div>
Design, evolve, and influence the Agent Delivery Framework.</div></li><li><div>
Establish and document agentic software development best practices; create reusable agent templates adopted across business teams.</div></li><li><div>
Build and maintain Knowledge Graph and RAG systems (Neo4j, Vectara) to enable document and parametric data retrieval across thousands of manufacturing documents and records.</div></li><li><div>
Collaborate with multiple departments, domain architects, and factory engineers to identify high ROI agent use cases, prioritize delivery, and ensure agents meet security and authentication requirements.</div></li><li><div>
Present architecture roadmaps and live agent demos to peers and senior manufacturing leadership.</div></li><li><div>
Contribute to cross-domain AI access and guardrails governance strategy.</div></li></ul></div>
</div></div></div></div></div></div><div></div><div><div><div><div><div><div><p>QUALIFICATIONS</p>
<div><div><b>Minimum Qualifications:</b></div>
<ul>
<li><div>Bachelor's degree in Computer Science, Software Engineering, Electrical Engineering, or a related technical field.</div></li>
<li><div>4+ years of software engineering experience, with at least 2 years focused on AI/ML systems or LLM application development.</div></li>
<li><div>Proficiency in Python and experience containerizing applications with Docker.</div></li>
<li><div>Hands-on experience building or integrating with REST API backends and asynchronous service architectures.</div></li>
<li><div>Strong communication skills &mdash; able to translate complex AI architecture decisions for non-technical manufacturing stakeholders.</div></li></ul><br>
<p></p>
<p><b>
Preferred Qualifications:</b></p>
<ul>
<li><div>Experience with multi-agent AI frameworks, agent orchestration patterns, or the MCP (Model Context Protocol) specification.</div></li>
<li><div>Familiarity with LiteLLM, Open Web UI, or similar LLM proxy/routing platforms.</div></li>
<li><div>Experience deploying workloads on Kubernetes (K8s) or cloud-adjacent enterprise platforms.</div></li>
<li><div>Working knowledge of graph databases (Neo4j) and vector search systems (Vectara, pgvector, or equivalent).</div></li>
<li><div>Prior exposure to semiconductor or discrete manufacturing data systems (MES, FDC, SPC, SCADA, APC) is a strong plus.</div></li>
<li><div>Experience with workflow automation tools (e.g. Airflow) and CI/CD pipelines (Bitbucket, Jenkins, or equivalent).</div></li>
<li><div>Master's degree or equivalent industrial AI research experience.</div></li>
</ul></div>
</div></div></div></div></div></div><div></div><div><div><div><div><div><div><p>ABOUT US</p>
<div><div><div><b>Why TI?</b>
</div><div><ul><li>Engineer your future. We empower our employees to truly own their career and development. Come collaborate with some of the smartest people in the world to shape the future of electronics.</li>
<li>We're different by design. Diverse backgrounds and perspectives are what push innovation forward and what make TI stronger. We value each and every voice, and look forward to hearing yours. Meet the people of TI</li>
<li>Benefits that benefit you. We offer competitive pay and benefits designed to help you and your family live your best life. Your well-being is important to us.</li><br>
</ul></div><p></p><br>
<div><p><b>About Texas Instruments</b>
</p><p>Texas Instruments Incorporated (Nasdaq: TXN) is a global semiconductor company that designs, manufactures and sells analog and embedded processing chips for markets such as industrial, automotive, data center, personal electronics and communications equipment. At our core, we have a passion to create a better world by making electronics more affordable through semiconductors. This passion is alive today as each generation of innovation builds upon the last to make our technology more reliable, more affordable and lower power, making it possible for semiconductors to go into electronics everywhere. Learn more at TI.com.<br>
</p><p></p><br>
<div><div>Texas Instruments is an equal opportunity employer and supports a diverse, inclusive work environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, creed, disability, genetic information, national origin, gender, gender identity and expression, age, sexual orientation, marital status, veteran status, or any other characteristic protected by federal, state, or local laws.<br>
</div><p></p><br>
<p>If you are interested in this position, please apply to this requisition.
</p></div></div></div></div></div></div></div></div></div></div><p></p><div><div><div><div><div><div><div>TI does not make recruiting or hiring decisions based on citizenship, immigration status or national origin. However, if TI determines that information access or export control restrictions based upon applicable laws and regulations would prohibit you from working in this position without first obtaining an export license, TI expressly reserves the right not to seek such a license for you and either offer you a different position that does not require an export license or decline to move forward with your employment.</div>
</div></div></div></div></div></div><div><div><div><div><div><div><p>JOB INFO</p>
<div><div><div><ul><div><div>Job Identification</div>
<div>25010490</div>
</div><div><div>Job Category</div>
<div>Information Technology</div>
</div><div><div>Posting Date</div>
<div>05/08/2026, 01:26 PM</div>
</div><div><div>Degree Level</div>
<div>Bachelor's Degree</div>
</div><div><div>Locations</div>
<div> <div></div><div><div>RFAB 300 W. Renner Road, Richardson, TX, 75080, US
</div></div></div></div><div><div>ECL/GTC Required</div>
<div>Yes</div></div></ul></div></div></div></div></div></div></div></div></div></div></div></div></div><p></p>

Agentic AI Engineer

ImagineArt

<div><h1 class="jobSectionHeader"><b>Principal AI Engineer (LLM Agents &amp; Orchestration)</b></h1><p><b><br>
Role Title:</b> Principal AI Engineer (LLM Agents &amp; Orchestration) <b>Team:</b> Chatly Engineering <b>Focus:</b> Building Autonomous &quot;Super Agents&quot;</p><h2 class="jobSectionHeader"><b><br>
Who We Are</b></h2><p>
Vyro is redefining the future of digital creativity. We build cutting-edge content creation tools powered by Artificial Intelligence and Machine Learning, helping millions of creators, designers, and storytellers bring their imagination to life &mdash; effortlessly.</p><p>
With a global user base of over 5 million active creators every month, Vyro&rsquo;s 20+ AI-powered apps are transforming how people design, edit, and express themselves across images, videos, and beyond.</p><p>
From intuitive AI photo editors to next-gen video creation platforms, our products are designed to make creativity accessible, fast, and limitless.</p><p>
At Vyro, we&rsquo;re a team of innovators, builders, and dreamers &mdash; known as Vyronauts &mdash; driven by passion, purpose, and the belief that technology should inspire creativity, not complicate it.</p><h3 class="jobSectionHeader"><b>
If you&rsquo;re excited about shaping the next wave of AI-powered creativity, Vyro is the place to be.</b></h3><h3 class="jobSectionHeader"><br>
<b>
About the Role</b></h3><p>
We are looking for a deep expert in Large Language Models (LLMs) to lead the architectural development of our new &quot;Super Agent&quot; within Chatly. You will move beyond simple chat interfaces to build autonomous agents capable of complex reasoning, tool usage, and seamless integration with external workflows.</p><h3 class="jobSectionHeader"><b>
Key Responsibilities</b></h3><ul><li><p><b>
Agent Architecture:</b> Design and implement stateful agentic workflows (using frameworks like LangGraph or custom Python/TypeScript solutions) that can plan, execute, and self-correct.</p></li><li><p><b>
Integration Ecosystem:</b> Build the &quot;hands&quot; of the agent. Develop robust integration layers that allow the LLM to interact with our internal APIs, databases, and third-party tools reliably.</p></li><li><p><b>
Latency &amp; Reliability:</b> Optimize inference pipelines for speed (streaming, token optimization) and reliability (handling hallucinations, structured output validation).</p></li><li><p><b>
Memory Systems:</b> Architect advanced RAG (Retrieval-Augmented Generation) systems to give the Super Agent persistent memory and context awareness across sessions.</p></li><li><p><b>
Evaluation &amp; Observability:</b> Establish a rigorous testing framework for non-deterministic model outputs to ensure the agent behaves as expected in production.</p></li></ul><h3 class="jobSectionHeader"><b>
Technical Requirements</b></h3><ul><li><p><b>
Core Stack:</b> Deep proficiency in Python (or TypeScript) and the modern AI stack (LangChain, LlamaIndex, DSPy).</p></li><li><p><b>
Model Internals:</b> Strong grasp of how to leverage specific model strengths (e.g., GPT-4o for reasoning, Haiku/Flash for speed) and experience with <b>Function Calling/Tool Use</b>.</p></li><li><p><b>
Vector Search:</b> Hands-on experience with vector databases (Pinecone, Milvus, Weaviate) and embedding strategies.</p></li><li><p><b>
System Design:</b> Experience designing event-driven architectures where agents respond to triggers, not just user prompts.</p></li></ul><p><b>
Why Join Us?</b></p><ul><li><p>
Opportunity to work on innovative AI products like <b>Chatly</b> and <b>Imagine</b>, shaping the future of creativity and user interaction.</p></li><li><p>
Be part of a passionate, fast-moving team that values innovation and data-driven decisions.</p></li><li><p>
Competitive compensation and benefits.</p></li><li><p>
A culture where learning, growth, and experimenting with new ideas are deeply encouraged.</p></li></ul></div><p></p>

Booz Allen Hamilton

<div>Agentic AI Engineer<p><b>
The Opportunity</b><b>:</b><br>
We are looking for a highly skilled Agentic AI Engineer to join our team specializing in building autonomous, goal-oriented AI systems. You will play a crucial role in shifting our AI strategy from passive LLM chatbots to proactive, multi-agent orchestrations. In this role, you will utilize deep experience in agent orchestration frameworks, RAG, knowledge graphs, and fine-tuning Small Language Models (SLMs) for edge deployment.<br>
<br>
<b>What You'll Work On:</b></p><ul><li><p>
Design and implement intelligent agent architectures that can reason, plan, and take actions using LangChain, LangGraph, and AutoGen.</p></li><li><p>
Develop and deploy multi-agent systems using Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols to facilitate communication, tool usage, and collaborative task solving.</p></li><li><p>
Build advanced RAG pipelines integrating unstructured data with Knowledge Graphs (KG) to enhance reasoning accuracy and context retention.</p></li><li><p>
Fine-tune SLMs for specific domains and optimize them for edge device performance, including ONNX, GGML, or Ollama.</p></li><li><p>
Develop evaluation frameworks to test agent reliability, safety, and performance, moving from prototype to production, including ReAct loops and human-in-the-loop.</p></li></ul><p></p><p>
Join us. The world can&rsquo;t wait.</p><p></p><p><b>
You Have:</b></p><ul><li><p>
4+ years of experience in software development</p></li><li><p>
3+ years of experience as an ML engineer building production-grade ML solutions using tools such as Docker or Kubernetes, including, GenAI, LLMs, DL, RL, AI agents, agentic workflows, or complex automation frameworks</p></li><li><p>
3+ years of experience with LangChain, LangGraph, AutoGen, PydanticAI, CrewAI, or LlamaIndex</p></li><li><p>
3+ years of experience working in cloud environments, including AWS and Azure and evaluating architectural tradeoffs and designing robust service-based software applications for scalable use</p></li><li><p>
Experience with MCP for tool integration and A2A for agent-to-agent collaboration and with RAG architecture and KG, including Neo4j or NebulaGraph</p></li><li><p>
Experience fine-tuning LLMs or SLMs using Hugging Face, PEFT, or LoRA, evaluating LLM performance and behavior through evaluations, and building observation layers for stakeholders, including Grafana, Langfuse, LangSmith, or Phoenix</p></li><li><p>
Knowledge of modern software design patterns, including microservice design or edge computing</p></li><li><p>
Ability to adapt in a rapidly changing environment and navigate ambiguity</p></li><li><p>
TS/SCI clearance with a polygraph</p></li><li><p>
Bachelor&rsquo;s degree</p></li></ul><p></p><p><b>
Nice If You Have:</b></p><ul><li><p>
Experience deploying agentic systems in a production environment</p></li><li><p>
Experience deploying agents on edge devices such as Android or local models</p></li><li><p>
Experience integrating coding agents such as Cursor or Windsurf into an efficient development pipeline with measured results</p></li><li><p>
Experience with programming, including ML frameworks such as TensorFlow, PyTorch, llama.cpp, and vLLM</p></li><li><p>
Experience engineering AI capabilities in on‑premise or multi‑classification environments</p></li><li><p>
Possession of excellent verbal and written communication skills for client engagements, client-facing project work, and business development</p></li><li><p>
Master&rsquo;s degree in a CS or AI field preferred; Doctorate degree in CS or Statistics a plus</p></li></ul><p></p><p><b>
Clearance:</b></p><p>
Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information; TS/SCI clearance with polygraph is required.</p><p></p><p><b>
Compensation</b></p><p>
At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen&rsquo;s benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page.</p><p></p>
Salary at Booz Allen is determined by various factors, including but not limited to location, the individual&rsquo;s particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $99,000.00 to $225,000.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen&rsquo;s total compensation package for employees. This posting will close within 90 days from the Posting Date.<p></p><p><b>
Identity Statement</b></p><p>
As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.</p><p></p><p><b>
Candidate AI Usage Policy</b></p><p>
AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on <b>your </b>own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited <b>unless permission is explicitly provided</b>.
</p><p></p><p><b>Work Model</b><br>
Our people-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings.</p><ul><li><p><b>
Remote</b>: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility.</p></li><li><p><b>
Hybrid</b>: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility.</p></li><li><p><b>
Onsite</b>: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role.</p></li></ul><p></p><p><b>
Commitment to Non-Discrimination</b></p><p>
All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.</p></div><p></p>

FocusKPI Inc.

<div><p>We are seeking an <b>Agentic AI Engineer</b> to support the development of a customized AI agent solution for one of our clients.<br>
<br>
We're a consulting team that helps clients design and build customized AI agent solutions. You'll work across engagements &mdash; understanding client problems, architecting the right agentic approach, and shipping working systems. This is not a research role. You'll be in the work &mdash; building agents that go live, with real clients, on real timelines.<br>
<br>
<b>Environment:</b><br>
<br>
We move fast and work across multiple client contexts. You'll need to be comfortable with ambiguity, adapt quickly across industries, and take ownership of your engagements without much hand-holding.<br>
<br>
If you thrive in structured, single-product environments with long planning cycles, this probably isn't the right fit. If you love the variety of consulting and want to build things that actually ship, let's talk.<br>
<br>
<b>Work Location:</b><b> </b>Remote position (40 hours per week)<br>
<b>Duration:</b> Full-time role<br>
<b>Pay-rate:</b> $30+/hr<br>
<br>
<b>**No C2C resumes are considered**</b><br>
<br>
<b>Responsibilities:</b></p><ul><li>
Consult with clients to understand their workflows, pain points, and where agentic AI can create real leverage</li><li>
Design and build custom AI agent solutions tailored to each client's stack, data, and constraints</li><li>
Own full delivery &mdash; from architecture and prompt design to tool integration, testing, and handoff</li><li>
Build evals and monitoring so clients can trust what's running in production</li><li>
Move across engagements &mdash; you'll context-switch between clients and industries</li></ul><p><b>
Qualifications:</b></p><ul><li>
A minimum of <b>2 years</b> of experience is required</li><li><b>
Education requirements:</b> Master's degree</li><li><b>
Mandatory skills:</b> Agentic AI, Claude, LLM</li><li>
Building production-grade agentic systems using frameworks like LangChain, LangGraph, CrewAI, AutoGen, or Claude Agent SDK</li><li>
Designing agent architectures: tool routing, memory, multi-agent coordination, retrieval pipelines</li><li>
Translating a client's messy real-world process into a structured agent design</li><li>
Communicating clearly with non-technical stakeholders &mdash; you can explain what you're building and why</li><li>
Delivering under real project constraints &mdash; timelines, client expectations, shifting requirements</li></ul><p><b>
Nice to have:</b></p><ul><li>
Experience building agents for GTM use cases &mdash; sales research, outreach automation, CRM workflows, lead enrichment, or marketing operations</li><li>
Comfort connecting agents to third-party APIs, CRMs (HubSpot, Salesforce), or communication tools</li></ul><p></p><p>
Thank you!</p><p>
FocusKPI Hiring Team</p><p>
Founded in 2010, FocusKPI, Inc. (FocusKPI) is a data science and technology firm specializing in predictive analytics practice and methodologies. FocusKPI is a US company headquartered in Silicon Valley, California, with an East Coast office in Boston, Massachusetts.</p><p>
</p><p><b>NOTICE: </b>Please be aware of fraudulent emails regarding job postings, job offers and fake checks. FocusKPI's recruiting team will strictly reach out via @focuskpi.com email domain. If you have received fraudulent emails now or in the past, please report it to https://reportfraud.ftc.gov/ .<br>
The domain @focuskpijobs.com is fraudulent and not related to FocusKPI. Please do not not reply or communicate to anyone with @focuskpijobs.com.</p><p>
</p><p>DC4HSZaJiV</p></div>

AR5 Labs

<p><b>AI Engineering Intern &mdash; AR5 Labs</b></p><p>Remote | Internship</p><p><b>ABOUT AR5 LABS</b></p><p>AR5 Labs builds AI-powered automation tools for data analytics. Our flagship product, PlotStudio AI, is a desktop analytics platform where specialized AI agents collaborate through a multi-stage pipeline to deliver the kind of analysis a senior data scientist would produce, complete with code, visualizations, statistical rigor, and executive-grade narrative. Think Cursor, but for data analytics.</p><p>Product: https://www.plotstudio.ai/</p><p>LinkedIn: https://www.linkedin.com/company/ar5-labs/</p><p><b>ABOUT THE ROLE</b></p><p>We're looking for an AI Engineering Intern to help build and improve the AI agent pipeline behind PlotStudio AI.</p><p>This is not a research internship. You'll work on production AI systems: prompt engineering, agent orchestration, model evaluation, and the infrastructure that makes multi-agent workflows reliable. You'll see how AI agents are actually built, tested, and shipped to real users.</p><p>You'll work directly with the founder and the AI/ML team. The codebase is small enough that your contributions will ship to production quickly, and complex enough that you'll learn how a real multi-agent system works from the inside.</p><p>If you're a student who wants to build AI systems, not just study them, this is the role.</p><p><b>RESPONSIBILITIES</b></p><p>- Help design, test, and iterate on prompts for PlotStudio's multi-agent pipeline (routing, planning, coding, interpretation agents)</p><p>- Build evaluation scripts to benchmark agent output quality across different datasets and question types</p><p>- Run experiments comparing model performance (GPT-4.1, GPT-5.1, Claude, Gemini) on specific analytical tasks</p><p>- Help improve agent self-correction logic: how the system detects errors, retries, and adjusts its approach</p><p>- Work on domain skill modules that inject specialized knowledge (finance, healthcare, econometrics) into the agent pipeline</p><p>- Build internal tooling for tracking agent accuracy, token usage, and failure modes</p><p>- Help extend the agent orchestration layer (built with LangGraph) to support new workflow types</p><p>- Document prompt versions, experiment results, and engineering decisions as you go</p><p><b>QUALIFICATIONS</b></p><p>Required:</p><p>- Currently pursuing a degree in Computer Science, Data Science, AI/ML, or a related field (rising sophomore, junior, or senior)</p><p>- Comfortable writing Python. You should be able to read, write, and debug code independently</p><p>- Basic understanding of how LLMs work: prompts, tokens, context windows, temperature</p><p>- Genuine interest in AI agents, not just chatbots. You should have an opinion on why single-prompt AI falls short for complex tasks</p><p>- Willingness to experiment, break things, and iterate quickly</p><p>- Self-directed and comfortable asking questions when you're stuck</p><p>- Able to commit 15-20 hours per week</p><p><b>Nice to have:</b></p><p>- Experience using the OpenAI API, Anthropic API, or any LLM SDK</p><p>- Familiarity with LangChain, LangGraph, CrewAI, or any agent framework</p><p>- Personal projects involving LLMs, prompt engineering, or AI agents</p><p>- Coursework in machine learning, NLP, or statistics</p><p>- Familiarity with Git and version control</p><p>- Experience with FastAPI, Flask, or any Python backend framework</p><p>- Understanding of statistical methods (regression, hypothesis testing, time series)</p><p><b>WHAT YOU'LL LEARN</b></p><p>- How a production multi-agent system is built: routing, planning, execution, self-correction, and interpretation agents working in sequence</p><p>- Prompt engineering at a level most people never see: versioned prompts, domain-aware injection, structured output parsing, and quality gates between agents</p><p>- How to evaluate AI systems beyond vibes: building benchmarks, measuring accuracy, tracking regressions across prompt versions</p><p>- How to ship AI to real users in a small, fast-moving team</p><p><b>WHY THIS INTERNSHIP</b></p><p>- You'll work on a real multi-agent AI system, not a tutorial project</p><p>- Small team means high visibility and direct mentorship from the founder</p><p>- Your work will ship to production and be used by real customers</p><p>- You'll have something concrete for your resume: &quot;I helped build and evaluate the AI agent pipeline for a production analytics platform&quot;</p><p><b>MUST HAVE</b></p><p>We are across the United States and Canada. Must be a citizen of either country.</p><p><b>HOW TO APPLY</b></p><p>Follow our LinkedIn page and DM us directly, or email us:</p><p>1. A short intro about yourself</p><p>2. Your resume or LinkedIn profile</p><p>3. What year you're in and what you're studying</p><p>4. Why you want to work at AR5 Labs</p><p><b>Email</b>: plotstudio@ar5labs.com</p><p><b>LinkedIn</b>: https://www.linkedin.com/company/ar5-labs/</p><p>Pay: $20.00 - $45.00 per hour</p><p>Work Location: Remote</p>

AI Engineer Intern

VeeRteq Solutions Inc.

<div><p>We are seeking a high-caliber <b>AI Tech Lead / AI Engineer</b> to design, build, and operationalize agentic AI solutions that accelerate analytics and reporting delivery. This role goes beyond basic agent creation and requires hands-on experience with agent orchestration, memory, and enterprise-grade data access patterns, delivering scalable components that fit into an existing framework while rapidly adapting to incoming business requests.</p>
<p></p><p><b>Responsibilities: -</b></p>
<ul><li>Design, Architect, implement, and iterate on agentic AI solutions to shorten the cycle time for analytics, log analysis, and reporting requests.</li>
<li>Design agent orchestration patterns (multi-agent workflows), tool/function calling, and memory approaches appropriate for enterprise deployments.</li>
<li>Define secure and scalable data access patterns for agents (retrieval, context building, and grounding), integrating with existing data sources and governance expectations.</li>
<li>Partner closely with product, analytics, and engineering stakeholders to intake requirements quickly and deliver working prototypes and production-ready solutions.</li>
<li>Engineer reusable components and best practices that enable scalable delivery (not one-off scripts), aligned to an existing base framework.</li>
<li>Operationalize solutions for reliability and maintainability: testing strategies, monitoring/observability, prompt/version management, and deployment automation.</li>
<li>Evaluate build vs. buy options pragmatically when needed, while keeping focus on shipping solutions on the current platform stack.</li>
</ul><p></p><p><b>Tech Stack (Core): -</b></p>
<ul><li><b>Cloud:</b> AWS (primary deployment environment; open to alternatives)</li>
<li><b>Data/Analytics Platform:</b> Databricks (including native &quot;chat with data&quot; capabilities and potential agent integrations)</li>
<li><b>Agent Frameworks:</b> LangChain, LangGraph</li>
<li><b>Conversational analytics patterns:</b> Ask-questions-on-data / conversational BI approaches (agent-driven analytics and dashboards</li>
</ul><p></p><p><b>Experience: -</b></p>
<p>10+ Years</p>
<p></p><p><b>Location: -</b></p>
<p>Rosemont, IL (3days/Week)</p>
<p></p><p><b>Educational Qualifications: -</b></p>
<p>Engineering Degree BE/ME/BTech/MTech/BSc/MSc.</p>
<p>Technical certification in multiple technologies is desirable.</p>
<p><b>Skills: -</b></p>
<p><b>Mandatory skills</b></p>
<ul><li>Demonstrated experience delivering agentic AI solutions beyond prototypes, including enterprise deployment considerations.</li>
<li>Strong hands-on engineering background with AWS-based deployments.</li>
<li>Experience working with modern data platforms (e.g., Databricks) and integrating LLM solutions with analytics/data ecosystems.</li>
<li>Ability to operate as a senior individual contributor who can define architecture and implement key pieces end-to-end.</li>
</ul><p>Excellent communication and collaboration skills with US-based stakeholders</p>
<p><b>Skills &amp; Expertise Needed</b></p>
<ul><li><b>Agentic </b><b>AI engineering:</b> building and deploying LLM-powered agents for real business workflows.</li>
<li><b>Agent orchestration:</b> designing multi-step and/or multi-agent flows; managing tool use, control flow, retries, and failure handling.</li>
<li><b>Agent memory:</b> short-term and long-term memory patterns; conversation state; summarization and context window management.</li>
<li><b>Enterprise data access patterns for agents:</b> retrieval/grounding strategies; context assembly from structured and unstructured sources; performance-conscious access.</li>
<li><b>Production deployment mindset:</b> security, reliability, monitoring, and maintainability for enterprise-grade AI services.</li>
<li><b>Architecture &amp; best practices:</b> ability to design scalable components that fit into an existing framework and can be extended by the team.</li>
<li><b>Rapid requirements intake:</b> quickly translating ambiguous reporting/analytics asks into implementable solutions and iterating with stakeholders.</li>
<li><b>High autonomy:</b> tech-lead level capability without direct people management; self-directed and able to set engineering direction for the workstream</li>
</ul><p></p><p><b>Good-to-Have Skills</b></p>
<ul><li>Experience implementing retrieval-augmented generation (RAG) and hybrid retrieval across structured/unstructured sources.</li>
<li>Experience with LLMOps practices: prompt/version management, automated evaluation, and regression testing.</li>
<li>Experience building observability for AI systems (quality metrics, traces, latency/cost monitoring).</li>
<li>Familiarity with Databricks-specific agent or model serving patterns (where applicable).</li>
</ul><p>Experience building lightweight analytics experiences on top of agent outputs (e.g., auto-generated insights/dashboards).</p></div>

AI Tech Lead / AI Engineer

Etera Technologies FZCO

<div><p><b>About Etera</b></p><p>
Etera is building the first AI-native corporate travel platform for the GCC market &mdash; designed from the ground up for how businesses in the region actually operate. We're a lean, high-conviction team replacing legacy TMCs with autonomous agents, real-time inventory, and a product experience that doesn't feel like it was designed in 2009.</p><p></p><p><b>
The Role</b></p><p>
We're hiring an Engineering Manager who owns all of Etera's engineering, across all four verticals (Hotels, Flights, Restaurants, Experiences) and the platform layer beneath them. This is not a meeting-heavy management role. You spend at least 50% of your time hands-on: writing code, reviewing architecture, debugging production issues, and shipping features alongside your team. The other 50% you spend making everyone around you dramatically more productive &mdash; through hiring, technical direction, and building the agentic coding infrastructure that turns a small team into a force multiplier.</p><p></p><p><b>
What You'll Own</b></p><ul><li>
All of engineering. Every vertical, every platform service, every microservice. You set the technical direction, own the architecture decisions, and are accountable for the quality and velocity of everything that goes to production. With a team of 3&ndash;5 engineers today (scaling to 8+), you are the engineering leadership &mdash; not one layer of it.</li><li>
The agent infrastructure. Etera runs a multi-team Claude Code agent architecture (OPUS Prime) &mdash; 8 teams, ~60 specialist agents, two-tier orchestration. You own this system as a core engineering platform. You maintain agent configurations, manage prompt drift, handle tool failures, design validation gates, and ensure agent output meets the same quality bar as human-written code. If you haven't operated agent systems at this scale before, that's fine &mdash; but you need to have built with agentic coding tools extensively enough that scaling to platform-level ownership is a natural next step, not a learning cliff.</li><li>
The 10x engineering culture. You are responsible for ensuring every engineer on your team operates at maximum leverage &mdash; meaning they know how to configure, orchestrate, and validate coding agents as part of their daily workflow. You don't just use agentic tools yourself. You build the playbooks, set the standards, and coach engineers who are still thinking in manual keystrokes to think in agent-augmented systems. The goal is a team where every engineer ships like a team of three.</li><li>
Hands-on engineering. At least 50% of your time is spent writing code, reviewing PRs, designing systems, and solving hard technical problems directly. You set the engineering standard through the work you do, not just the direction you set. When a critical supplier integration breaks at 11pm, you're in the code, not waiting for a status update.</li><li>
Delivery. You break work into lean epics &mdash; problem statements, success metrics, scope boundaries. You route tasks between human engineers and agent teams based on complexity and judgment requirements. You don't write granular tickets that create the illusion of progress. You compress work aggressively and hold people to outcomes, not activity.</li><li>
Hiring. You build the team. You source, interview, evaluate, and make the call. You know the difference between a CV that was written by Claude and a career that was built by a real engineer &mdash; because you've already seen both.</li></ul><p></p><p><b>
Tech Stack</b></p><ul><li>
Backend: TypeScript (end-to-end), 26 microservices across 4 verticals + platform, OpenSearch v3, REST + Webhook orchestration, supplier integrations (PKfare, RateHawk, Hotelbeds, Viator).</li><li>
Frontend / Mobile: React Native + Expo, TypeScript, Firebase Auth (OTP, Apple, Google).</li><li>
AI / Agent Layer: Anthropic API / Claude, Claude Code (agent teams as default runtime), Mastra (agent orchestration), Context7, LightRAG (Stage 2/3).</li><li>
Infrastructure: AWS (UAE data residency), EAS Build / TestFlight, CI/CD pipelines.</li></ul><p></p><p><b>
You Write Code. You Run Teams. You Build the Machine That Builds the Product.</b></p><p>
This role lives at the intersection of three things. The strongest candidates will bring depth in all three. Realistically, we expect deep strength in the first two and genuine momentum in the third.</p><p><b>
Dimension 1: You are a strong hands-on engineer.</b></p><p>
You have shipped production systems in TypeScript, backend services, APIs, mobile features. You can review a database migration, debug a webhook integration, and refactor a React Native component in the same afternoon. You're in the codebase daily, and your team trusts your technical judgment because they see you make hard calls in code, not just in meetings.</p><p><b>
Dimension 2: You have led engineering teams that ship.</b></p><p>
You have managed 3+ engineers in a production environment. You've hired, run 1:1s, handled performance issues, and made the call on when to ship and when to hold. You know how to set direction without micromanaging, how to give feedback that actually changes behavior, and how to build a team culture where people do their best work. You've done this in a remote or distributed setting &mdash; async communication and written-first culture are your default.</p><p><b>
Dimension 3: You are building deep fluency with agentic engineering tools.</b></p><p>
The ideal candidate has already built or managed multi-agent coding systems in production. But we also want to hear from engineering leaders who have been using agentic tools (Claude Code, Codex, Cursor, or equivalent) extensively as power users, shipping real features through them daily &mdash; and are ready to take ownership of a platform-level agent infrastructure. What matters is that you've moved well past experimentation into genuine fluency, and that scaling agent systems excites you rather than intimidates you.</p><b><br>
Requirements</b><p><b><br>
Requirements</b></p><ul><li>
7+ years of professional software engineering, with at least 2 years managing a team and owning delivery.</li><li>
TypeScript across the full stack &mdash; you read, write, review, and architect in TypeScript. Non-negotiable.</li><li>
Production experience with LLM APIs (Anthropic, OpenAI, or equivalent) &mdash; tool calling, multi-turn management, evaluation pipelines, failure handling. Not just prompting.</li><li>
Hands-on daily use of agentic coding tools (Claude Code, Codex, Cursor, or equivalent). You ship real work through them, not side experiments. Bonus if you've built systems, playbooks, or workflows that helped other engineers adopt them.</li><li>
Experience managing remote or distributed engineering teams. Written-first, async-native communication.</li><li>
Strong written English. You write specs, design docs, architectural decisions, and agent configurations. If you can't write clearly, you can't lead clearly. Arabic or German is a genuine plus.</li></ul><p></p><p><b>
Bonus Points</b></p><ul><li>
Prior work with travel supplier APIs or GDS/NDC systems (Amadeus, Sabre, PKfare, RateHawk).</li><li>
Experience with Voice AI systems (Ultravox, Vapi, ElevenLabs) &mdash; building or managing teams that build automated voice calling.</li><li>
Familiarity with OpenSearch, vector search, or hybrid search systems.</li><li>
Experience building engineering teams from scratch in new geographies.</li><li>
Background in security auditing, compliance, or data residency requirements (UAE PDPL context).</li><li>
You've designed or maintained agent orchestration architectures &mdash; not just individual agents, but the coordination layer between them.</li></ul><br>
<b>
Benefits</b><p><b><br>
What We Offer</b></p><ul><li>
Greenfield Ownership &mdash; You own all of engineering. Not a slice. The whole thing.</li><li>
Human + AI Engineering &mdash; You'll run one of the first hybrid engineering organizations where autonomous agents are first-class members of the team, not side tools. You'll define how this works.</li><li>
Remote-First &mdash; Work from anywhere. Async-friendly, written-first culture. Team distributed across Europe, Pakistan, and the Gulf.</li><li>
Equity + Competitive Package &mdash; Meaningful equity stake. Competitive cash. Built for the long term.</li><li>
High-Impact Market &mdash; GCC corporate travel is a $20B+ market running on legacy infrastructure. You're building the replacement.</li><li>
50% Hands-On &mdash; This isn't a role where you slowly drift into full-time meetings. The expectation is baked in: you write code, you review code, you ship code. That's protected time, not aspirational.</li></ul></div>

Engineering Manager

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