Must Have Technical / Functional Skills

• Core Engineering & Programming: Strong software engineering fundamentals with expert-level proficiency in Python. Experience with Java, Go, or TypeScript is a strong plus.

• LLM & GenAI Application Development: Proven, hands-on experience building and deploying production-grade applications using Large Language Models (LLMs) like GPT, Claude, or Gemini.

• This must go beyond simple API calls and include experience with tool/function-calling, structured outputs, and evaluation.

• Agentic AI Frameworks & Orchestration: Demonstrable expertise in designing and implementing agentic workflows using frameworks like LangGraph, Semantic Kernel, AutoGen, or similar.

• Experience with multi-agent systems, planning, and autonomous execution is critical.

• RAG (Retrieval-Augmented Generation): Deep, practical knowledge of building and optimizing RAG pipelines. This includes data ingestion, various chunking strategies, embeddings, vector databases (e.g., Pinecone, Chroma, FAISS), and hybrid search/reranking.

• Production & MLOps: Experience with production engineering practices, including building scalable APIs (REST, RPC), microservices, CI/CD pipelines, containerization (Docker, Kubernetes), and cloud platforms (AWS, Azure, or GCP).

Roles & Responsibilities

• Agentic System Design & Engineering: Architect, build, and deploy advanced AI agents capable of autonomous reasoning, decision-making, and self-directed task execution. Design and implement complex, multi-step agentic workflows that integrate with enterprise APIs, data sources, and platforms.

• RAG and Grounding Implementation: Develop robust RAG pipelines to ground agent responses in factual, reliable data. This includes managing the full lifecycle from data ingestion and vectorization to retrieval and citation.

• Tooling and Integration: Build and maintain the "tools" that agents use to interact with the digital world. Create secure, well-documented tool interfaces for internal services, databases, and third-party APIs.

• Evaluation, Guardrails & Safety: Design and implement comprehensive evaluation frameworks to measure agent performance, accuracy, and reliability.

• Develop and enforce safety guardrails, policy checks, and fallback mechanisms to ensure agents operate safely and predictably in production environments.

• Optimization and Productionization: Debug, monitor, and optimize agentic systems for latency, cost, and efficiency. Own the end-to-end deployment process, including CI/CD, structured logging, and incident response for AI systems.

Generic Managerial Skills, If any

• Problem-Solving & Critical Thinking: Ability to analyze complex, ambiguous problems and design innovative, practical solutions. Thrives in navigating the uncertainty inherent in emerging AI technologies.

• Collaboration & Communication: Excellent communication skills with the ability to articulate complex technical concepts to both technical and non-technical stakeholders. Proven experience working cross-functionally with product, research, and infrastructure teams.

• Ownership & Leadership: A bias for action and a strong sense of ownership. Capable of driving projects from conception to completion, mentoring junior engineers, and helping to define and influence AI strategy and best practices

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