Senior AI Agentic Engineer

• Design, build, and deploy end-to-end agentic AI systems 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 of agentic solutions with enterprise systems including ERP, Business apps and orchestration platforms such as Databricks, Airflow, and Azure Data Factory.

• Design and optimize RAG 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.
• Optimize prompts, system instructions, and output schemas for reliability, determinism, and safety across agentic pipelines.
• Apply reinforcement or feedback-driven optimization where applicable, including human-in-the-loop and automated evaluation loops.

• Define evaluation frameworks for agentic systems 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 responsible AI practices, audit traceability, data privacy, and regulatory adherence across all deployed agents.

• Deploy GenAI and agentic systems into production using cloud-native architectures on platforms such as Azure, AWS Bedrock, or Google Vertex AI with containerized (Docker/Kubernetes) delivery.
• Implement CI/CD pipelines, prompt versioning, rollback strategies, and runtime safeguards for LLM applications in enterprise environments.
• Optimize deployed 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-structured agentic solutions.
• Mentor AI engineers and data scientists on agentic design patterns, responsible AI practices, and production-grade engineering standards.
• Contribute to the organization's AI automation strategy, co-authoring technical roadmaps, governance policies, and center-of-excellence standards for agentic AI.
• Stay at the forefront of the agentic AI landscape, rapidly evaluating new frameworks and research findings and communicating their business relevance to leadership.

• Proven enterprise experience architecting and deploying production-grade multi-agent AI systems that automate real business workflows end-to-end — not just proofs of concept.
• Deep hands-on expertise with agent orchestration frameworks such as LangChain, LangGraph, AutoGen, Semantic Kernel, DSPy, CrewAI, and platform-native solutions such as Databricks AgentBricks / Mosaic AI Agent Framework — with openness to emerging tools in the rapidly evolving ecosystem.
• Deep understanding of LLMs and foundation models (e.g., GPT, Claude, Llama, Mistral, Gemini) including their capabilities, limitations, and appropriate use case fit.
• Experience with structured outputs, function/tool calling, JSON schema design, and multi-turn agent loop engineering.

• 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 and AI pipelines.
• Experience with database technologies, data lakes, and enterprise data platforms including SQL, cloud storage, and streaming data sources that agents consume at runtime

• Strong Python proficiency and experience building production-grade services, APIs, and microservices that support agentic systems.
• Experience deploying LLM and agent workloads on cloud platforms (e.g., Azure OpenAI Service, AWS Bedrock, Google Vertex AI) with containerized infrastructure (Docker, Kubernetes).
• Experience implementing LLMOps practices including experiment tracking (e.g., MLflow, W&B), prompt versioning, evaluation harnesses, latency profiling, and CI/CD for AI systems.
• Experience with enterprise security, data governance, and compliance requirements for AI deployments including PII handling, role-based access control, and audit logging.

• Familiarity with LLM evaluation techniques, failure mode analysis, red-teaming, and benchmark construction to maintain quality and trust in production agents.
• Working knowledge of responsible AI principles including fairness, explainability, safety guardrails, and human oversight mechanisms in agentic deployments.

• Strong written and verbal communication skills with the ability to explain complex GenAI and agentic concepts clearly to both technical teams and executive stakeholders.
• Demonstrated ability to lead cross-functional AI projects from discovery through production, aligning engineering, data, product, legal, and business operations teams.
• Ability to mentor junior team members, establishing engineering standards and fostering a culture of experimentation and responsible AI development.
• Ability to translate ambiguous, open-ended business challenges into structured agentic 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.

• 3 years' work experience as an AI Agentic Engineer and over 7 years' experience in Data Science, Gen AI, Information Systems, Computer Science, Software Engineering or other relevant field with relevant experience.

• Preferred - Master's Degree in Data Science, Data Analytics, Information Systems, Computer Science, Engineering or other relevant field.
• Required – bachelor's degree in data science, Information Systems, Computer Science, Engineering or other relevant field with relevant experience.

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