Agentic AI Engineer

1 week ago

Bengaluru, India Lara Tech Consulting Full time

Agentic AI Engineer Experience: 4–7 years Location: Bangalore / New Delhi / Gurugram Availability: This is an urgent requirement – Immediate joiners preferred 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.

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