Agentic AI Engineer

The SeniorAIAgenticEngineer designs, builds, and operationalizes intelligent agent systems that automate complex enterprise business processes end-to-end. This role works at the intersection of LLMs, systems engineering, and applied machine learning — architecting multi-agent pipelines, tool‑augmented reasoning systems, and retrieval‑augmented generation (RAG) workflows across a range of enterprise platforms (e.g., DatabricksAgentBricks, Azure OpenAI) and open‑source frameworks (e.g.,LangGraph,AutoGen) — with the expectation that the right candidate brings familiarity with the broader and rapidly evolving ecosystem. The ideal candidate brings deep hands‑on engineering experience with a proven track record of deliveringagenticAIsystems into production at enterprise scale — not just prototypes — applying rigorous software engineering principles including modular system design, testability, resilience engineering, and security‑by‑design to ensure agents are maintainable, reliable, and safe in the long run. This means architecting for failure — building in retries, fallbacks, and graceful degradation — and treating latency and cost as first‑class engineering constraints from day one, not afterthoughts discovered in production. Beyond technical delivery, the SeniorAIAgenticEngineermentorsengineers across the team, shapes the organization’sAIautomation strategy, translates ambiguous business problems into well‑structuredagenticsolutions, and drives the responsible and secure deployment ofAIagents across business‑critical functions. JobDuties/Roles AgenticAISystem Design & Development Design, build, and deploy end‑to‑endagenticAIsystems using LLMs, tools, memory, and planning frameworks to automate complex, multi‑step enterprise business processes. Architect and implement both single‑agent and multi‑agent workflows for autonomous task execution, decision support, and orchestration — defining agent roles, memory strategies, tool integrations, and handoff protocols. Develop tool‑using agents with function‑calling, structured outputs, API integrations, database connectors, RPA hooks, and enterprise workflow triggers. Lead the integration ofagenticsolutions with enterprise systems including ERP, Businessappsand orchestration platforms such as Databricks,Airflow, and Azure Data Factory. Retrieval‑Augmented Generation (RAG) Design and optimiseRAG pipelines including document ingestion, chunking strategies, embedding models, vector store selection, and retrieval ranking for enterprise knowledge bases. Implement advanced retrieval techniques such as hybrid search, metadata filtering, re‑ranking, and query rewriting to improve grounding and reduce hallucination. Evaluate and continuously tune RAG systems for accuracy, latency, factual grounding, and cost efficiency. Evaluate and select frontier and open‑source LLMs (e.g., GPT‑4o, Claude, Llama, Mistral, Gemini) and apply fine‑tuning strategies — including instruction tuning appropriate to each business use case. Optimiseprompts, system instructions, and output schemas for reliability, determinism, and safety acrossagentic pipelines. Apply reinforcement or feedback‑driven optimization where applicable, including human‑in‑the‑loop and automated evaluation loops. Evaluation, Monitoring & Governance Define evaluation frameworks foragenticsystems covering task success, factuality, grounding, latency, cost, and failure mode analysis. Build observability and monitoring pipelines for agent behavior, tool call traces, and runtime failure detection. Partner with governance, risk, and compliance teams to ensure responsibleAIpractices, audit traceability, data privacy, and regulatory adherence across all deployed agents. Production Deployment &LLMOps Deploy GenAI andagenticsystems into production using cloud‑native architectures on platforms such as Azure, AWS Bedrock, or VertexAI with containerised (Docker/Kubernetes) delivery. Implement CI/CD pipelines, prompt versioning, rollback strategies, and runtime safeguards for LLM applications in enterprise environments. Optimisedeployed systems for performance, cost efficiency, and scalability under real‑world load. Collaborate with software engineers, product managers, data scientists, and business stakeholders to translate ambiguous process challenges into well‑structuredagentic solutions. MentorAIengineers and data scientists onagentic design patterns, responsibleAIpractices, and production‑grade engineering standards. Contribute to the organization’sAIautomation strategy, co‑authoring technical roadmaps, governance policies, and center‑of‑excellence standards foragenticAI. Stay at the forefront of theagenticAIlandscape, rapidly evaluating new frameworks and research findings and communicating their business relevance to leadership. Knowledge,Skillsand Abilities Required (KSAR) Technical — AgenticFrameworks & LLMs Proven enterprise experience architecting and deploying production‑grade multi‑agentAIsystems that automatereal businessworkflows end‑to‑end — not just proofs of concept. Deep hands‑onexpertisewith agent orchestration frameworks such as,LangGraph,AutoGen, Semantic Kernel,DSPy,CrewAI, and platform‑native solutions such as DatabricksAgentBricks/ MosaicAIAgent Framework — with openness to emergingtools in the rapidly evolving ecosystem. Deep understanding of LLMs and foundation models (e.g., GPT, Claude, Llama, Mistral, Gemini) including their capabilities, limitations, andappropriate usecase fit. Experience with structured outputs, function/tool calling, JSON schema design, and multi‑turn agent loop engineering. Technical — RAG & Data Platforms Strong knowledge of RAG architectures, vector databases (e.g., Pinecone,Weaviate, Chroma,pgvector), embedding models, and hybrid retrieval strategies. Hands‑on experience with Databricks including Unity Catalog,MLflow, Delta Lake, and Databricks Workflows for end‑to‑end data andAIpipelines. Experience with database technologies, data lakes, and enterprise data platforms including SQL, cloud storage, and streaming data sources that agents consume at runtime Technical — Deployment,MLOps& Engineering Strong Pythonproficiencyand experience building production‑grade services, APIs, and microservices that supportagenticsystems. Experience deploying LLM and agent workloads on cloud platforms (e.g., Azure OpenAI Service, AWS Bedrock, VertexAI) with containerised infrastructure (Docker, Kubernetes). Experience implementingLLMOpspractices including experiment tracking (e.g.,MLflow, W&B), prompt versioning, evaluation harnesses, latency profiling, and CI/CD forAIsystems. Experience with enterprise security, data governance, and compliance requirements forAIdeployments including PII handling, role‑based access control, and audit logging. Evaluation & ResponsibleAI Familiarity with LLM evaluation techniques, failure mode analysis, red‑teaming, and benchmark construction to maintain quality and trust in production agents. Working knowledge of responsibleAIprinciples including fairness, explainability, safety guardrails, and human oversight mechanisms inagenticdeployments. Leadership & Communication Strong written and verbal communication skills with the ability to explain complex GenAI andagenticconcepts clearly to both technical teams and executive stakeholders. Demonstrated ability to lead cross‑functionalAI projects from discovery through production, aligning engineering, data, product, legal, and business operations teams. Ability to mentor junior team members,establishingengineering standards and fosteringa culture of experimentation and responsibleAIdevelopment. Ability to translate ambiguous, open‑ended business challenges into structuredagentic solution designs with clear scope and success criteria. Display an entrepreneurial mindset with a bias for practical, high‑impact solutions; comfortable operating in ambiguous environments and rapidly evolving technology landscapes. Working knowledge of Health, Safety, Quality and Environmental Management System. Minimumyears of Experience 3 years’ work experience as anAIAgenticEngineer and over 7 years' experience in Data Science, GenAI, Information Systems, Computer Science, SoftwareEngineeringor other relevantfieldwith relevant experience. Required/Preferred EducationRequirements Preferred -Master’s Degree in Data Science, Data Analytics, Information Systems, Computer Science, Engineeringorotherrelevantfield. Required - bachelor’s degree in data science, Information Systems, Computer Science,Engineeringor other relevantfieldwith relevant experience. This is a direct hire opportunity. The selected candidate will be employed directly by our client. All compensation and benefits, including but not limited to medical insurance, retirement plans, paid time off, and other perks, will be provided by the client in accordance with their internal policies and subject to applicable laws and eligibility requirements. #J-18808-Ljbffr Collabera