Job Description

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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)

Skills Required