AI Developer

Job Overview:

We are seeking a seasoned AI Developer to lead the design and implementation of next-generation Agentic AI solutions. You won't just be building chatbots; you will be architecting autonomous systems where multiple specialized AI agents collaborate to solve complex, long-horizon business problems. You will leverage a multi-cloud strategy to deploy scalable, resilient, and "reasoning-heavy" AI workflows.

 Key Responsibilities

• Agentic Architecture: Design and develop autonomous agents capable of independent planning, tool-use (function calling), context & prompt engineering, and self-correction.


• Multi-Agent Orchestration: Implement LangGraph to manage task delegation, conflict resolution, and collaborative reasoning between specialized agents.


• Advanced RAG & Memory: Build sophisticated Retrieval-Augmented Generation (RAG) pipelines with episodic and semantic memory to ensure agents maintain context over long interactions.


• Cloud-Native Deployment: Architect and deploy AI services across AWS (Bedrock/SageMaker), Azure (OpenAI Service/Foundry), ensuring high availability and cost optimization.


• Guardrails & Ethics: Implement rigorous evaluation frameworks (e.g., Ragas, TruLens) and safety guardrails to prevent hallucinations and ensure responsible agent behavior.


• Performance Tuning: Optimize LLM latency and throughput using techniques like prompt caching, quantization, and specialized model routing.


• Prompt engineering & Context engineering.

Key Skills:

• Core AI: 4+ years of professional experience in AI/ML, with at least 1.5+ years focused specifically on Generative AI and LLM orchestration.


• Agent Frameworks: Hands-on expertise with LangGraph. Proven ability to build "loops" and "state machines" rather than just linear chains.


• Programming: Mastery of Python (Asyncio, Pydantic, FastAPI). Experience with TypeScript/Node.js is a significant plus.


• Cloud Infrastructure:


• AWS: Bedrock, Lambda, Step Functions, S3.


• Azure: Azure OpenAI, AI Search, CosmosDB, PostgreSQL.


• Data & Memory: Proficiency with Vector Databases like Pinecone, Weaviate, Milvus, or pgvector. Understanding of graph databases (Neo4j) for knowledge-graph-enhanced RAG.


• DevOps/AIOps: Experience with Docker, Kubernetes, and CI/CD pipelines specifically for AI (LLMOps).

 Preferred Qualifications

• Contributions to open-source AI frameworks or research publications in MAS.


• Experience with Model Context Protocol (MCP) or Agent-to-Agent (A2A) communication standards.


• Familiarity with fine-tuning techniques (LoRA, QLoRA) for specialized agent tasks.