Agentic AI Engineer

Agentic AI Engineer

Experience:

Location:

Availability:

Overview

Design, build, and operate production-grade AI agents and tools using Agentic AI frameworks in Python. You will own agentic workflows end-to-end across planning, reasoning, tool calling, retrieval, evaluation, security, and observability.

Key Responsibilities

• Agent Development:

  Implement agentic workflows in Python using an agent framework (Semantic Kernel preferred; LangGraph, AutoGen, or CrewAI acceptable).

• Tool Orchestration:

  Build reliable tool and function-calling flows with planning, memory, and conversation orchestration.

• Retrieval Pipelines:

  Develop retrieval pipelines for RAG and hybrid search, including ingestion, chunking, embeddings, ranking, query planning, grounding, and caching.

• Cloud Deployment:

  Deploy agents to production on cloud platforms, integrating identity, networking, cost controls, and runtime observability.

• Monitoring & Observability:

  Instrument applications with tracing, metrics, and logs to ensure performance and reliability.

• Evaluation Workflows:

  Establish evaluation workflows using prompt and flow tests for offline, batch, and A/B scenarios.

• System Hardening:

  Collaborate with product, data, and security teams to harden systems using rate limits, retries, timeouts, and circuit breakers.

• Documentation & Mentorship:

  Maintain clear technical documentation and provide mentorship to peers on best practices and debugging.

Must-Have Qualifications

• 4–5 years of hands-on

  GenAI application experience

  before transitioning into agentic system work.
• Strong proficiency in

  Python 3.11+

  (typing, asyncio, packaging, testing with pytest, profiling, CI/CD).
• Production experience with at least one

  agent framework

  (

  Semantic Kernel preferred

  ; LangGraph/AutoGen/CrewAI acceptable).
• Proven expertise in

  tool/function calling

  , schema design, argument validation, and multi-step planning.
• Experience with

  retrieval systems

  using vector stores and hybrid search, including grounding and retrieval evaluation.

• Cloud deployment experience

  with containers, secrets/identity management, networking, monitoring, and alerting.
• Strong skills in

  observability and evaluation

  (tracing, metrics, log aggregation, experiment design, promotion criteria).
• Solid understanding of

  security and safety fundamentals: P

  rompt-injection defenses, content policy enforcement, sandboxing, and PII handling.
• Clear and effective

  technical communication

  with strong collaborative and review practices.

Good-to-Have Skills

• Multi-agent patterns (task decomposition, coordinator–worker, human-in-the-loop).
• Deeper Azure experience, including

  Azure AI Search

  and related AI platform services.
• Evaluation experience with

  regression suites, red teaming, and guardrails

  .
• Proficiency in search/data stores (Elasticsearch, Pinecone, pgvector, Azure AI Search).
• Frontend integration for

  agent UIs

  with streaming/tool traces and secure API design.

• DevOps proficiency

  with Docker, Kubernetes, GitHub Actions/Azure DevOps, IaC, and secrets management.