About the position

We are seeking a hands-on Python AI Engineer with expertise in prompt engineering, agentic AI systems, and LLM-driven applications. The ideal candidate will design, develop, and productionize AI-enabled features—from retrieval-augmented generation (RAG) pipelines to autonomous multi-agent workflows—integrating with internal tools, APIs, and enterprise systems.

Responsibilities

• Design & Build AI Services: Develop Python-based backend services integrating LLMs for reasoning, summarization, extraction, and decision support.
• Prompt Engineering: Craft, version, and optimize prompts/system instructions; implement guardrails, test variants, and improve reliability, latency, and cost-efficiency.
• Agentic Systems: Architect autonomous/multi-agent workflows with planning, tool-use, memory, error recovery, and human-in-the-loop controls.
• RAG Pipelines: Implement document ingestion, chunking, embeddings, vector search (semantic/re-ranking), and grounding strategies.
• Evaluation & Observability: Define metrics and build evaluation suites for accuracy, factuality, and safety; establish tracing and telemetry for LLM calls.
• API & Tool Integrations: Enable agents to use internal APIs, databases, and workflow engines; handle authentication, rate limits, and fallbacks.
• MLOps / AIOps: Package, containerize, and deploy services (Docker/Kubernetes); manage keys, secrets, CI/CD, canary rollouts, and cost governance.
• Security & Compliance: Apply data privacy principles, handle PII/redaction, enforce prompt injection defenses, and maintain audit logs.
• Cross-Functional Collaboration: Partner with product, data, and security teams to translate requirements into reliable, production-ready AI features.

Requirements

• Strong Python skills (typing, async, testing, packaging) and experience building production APIs/services (FastAPI, Flask).
• Hands-on experience with LLMs (OpenAI, Azure OpenAI, Anthropic, etc.) and embedding/RAG workflows.
• Proven prompt engineering experience (few-shot strategies, tool-use instructions, output schemas, function/tool calling).
• Experience with agent frameworks or custom orchestration (e.g., LangGraph, LangChain, AutoGen, or in-house equivalents).
• Experience with vector databases (FAISS, Chroma, Pinecone, Weaviate) and search relevance tuning.
• Familiarity with MLOps/DevOps: Docker, CI/CD, monitoring (Prometheus/Grafana), logging (OpenTelemetry), and secrets management.
• Experience with testing and evaluation: unit/integration tests, offline evaluations, golden datasets, regression checks.
• Practical understanding of AI safety and guardrails (prompt injection, data leakage, jailbreak prevention).

Nice-to-haves

• Experience with Azure/AWS/GCP AI services, key vaults, and networking.
• Knowledge of Model Context Protocol (MCP) or secure tool-server patterns.
• Familiarity with retrievers (BM25, hybrid search), re-rankers, or LlamaIndex/LangChain.
• Experience with streaming UIs and structured outputs (JSON, Pydantic schemas).
• Background in LLM fine-tuning, RLHF/DPO, or synthetic data generation.
• Front-end experience for AI UX (React/Next.js, chat UI patterns).
• Domain knowledge in HR/ATS, customer support, or internal enterprise workflows.

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