GenAI Engineer

Key Responsibilities

Design & Build Agentic AI Systems: Develop autonomous AI agents for planning, reasoning, and executing multi-step tasks with minimal human intervention.

Workflow Automation: Identify and automate repetitive business processes using AI agents to improve efficiency.

Developer Productivity Tools: Build AI-powered solutions for code generation, debugging, refactoring, and documentation across SDLC.

Business Impact: Collaborate with stakeholders to design AI solutions that improve KPIs such as revenue, cost optimization, and customer experience.

Enterprise Integration: Develop APIs and integrate AI agents with enterprise platforms, databases, and collaboration tools.

Multi-Agent Orchestration: Implement frameworks to coordinate multiple AI agents for complex workflows.

Ethics & Safety: Apply guardrails, monitoring, and security measures to ensure responsible AI usage and prevent adversarial attacks.

Continuous Innovation: Stay updated on advancements in LLMs, agentic AI, and emerging technologies to enhance solutions.

Required Skills & Experience

Experience: 4+ years in AI/ML development, focusing on agentic or autonomous systems.

Programming: Strong expertise in Python and AI/ML libraries (PyTorch, TensorFlow, Hugging Face).

Deep knowledge of LLM ecosystems (e.g., Azure OpenAI/OpenAI, Anthropic, Google, Meta): prompting, function calling, MCP (Model Context Protocol), Agent-to-Agent (A2A), token/cost management.

Expertise in RAG and Memory systems: vector DBs (FAISS, Pinecone, Milvus, pgvector/Postgres, Elastic/OpenSearch), embedding strategies, and rerankers.

Understanding of LLMOps and eval tooling: LangSmith, TruLens, Ragas, DeepEval, W&B, MLflow; prompt caching/compression; distillation.

Agentic Frameworks: Hands-on experience with AI agent orchestration tools and multi-agent coordination - LangGraph, AutoGen, Sematic Kernel, CrewAI (or similar) and tool integrations.

API Development: Proficiency in FastAPI/Flask and system integration.

Hands-on with coding agents & IDEs: GitHub Copilot, Cursor, Claude Code etc. and IDE integrations (VS Code/IntelliJ/JetBrains); expert in vibe coding workflows.

Cloud Platforms: Familiarity with AWS, Azure, or GCP for AI deployment.

Soft Skills: Strong problem-solving, communication, and ability to translate technical concepts for business teams

Education & Certifications

Bachelor s/Master s in Computer Science, Software Engineering, Data/AI, or related field (or equivalent experience).

Preferred: ML/AI certifications (Azure/AWS/GCP)