AI ENGINEER

AI ENGINEER AGENTIC SYSTEMS · A4I Lab (IIIT-B × Microsoft) Organization: A4I Lab (IIIT-B × Microsoft) Location: IIIT Bangalore Type: Contract (through March 2027, extendable) Experience: 3 to 5 years About A4I Lab AI Innovation and Inclusion Initiative (A4I) is a partnership between Microsoft and IIIT-B, collaborating with non-profit partners to harness AI for solving real-world challenges in education, healthcare, accessibility, and agriculture. A4I evolves innovations into open-source Digital Public Goods (DPGs) that are deployable at scale, fostering a strong AI innovation ecosystem for social impact. The Opportunity We are looking for an experienced AI-first engineer to join our core team in developing large-scale agentic AI solutions and integrating them with live systems that deliver meaningful social impact.

This role demands expertise in building intelligent agentic systems, orchestrating complex AI workflows, polishing existing AI and RAG-based responses, and developing scalable platforms that integrate LLMs, multi-modal inputs, and RAG-based architectures. You will play a key role in driving end-to-end innovation — from research to deployment — working with cross-functional teams that include architects, data scientists, and researchers from partner organizations. If shipping production-grade agentic pipelines that matter excites you, read on.

What You Will Be Doing Building Agentic AI Systems Build and integrate agentic AI pipelines using frameworks such as LangGraph, LlamaIndex, AutoGen, CrewAI, or n8n — working with multi-step reasoning, tool-call chains, and reflection patterns in real-world environments. Prototype and iterate on event-driven, tool-augmented agents with persistent memory (episodic stores, knowledge graphs), and help set up basic observability (tracing, latency monitoring, failure alerting) to keep systems reliable. Improve and polish existing AI and RAG-based responses — applying prompt engineering techniques (chain-of-thought, few-shot, structured outputs), retrieval tuning, and evaluation tools (RAGAS, TruLens, LangSmith) across multi-modal and multilingual workloads.

Integrating Intelligent Data Pipelines Work across the ML lifecycle — from RAG architecture and vector store setup (Chroma, Weaviate, Pinecone, pgvector) to containerised deployment with Docker and supporting model-serving pipelines on cloud platforms. Help integrate LLMs (GPT-4o, Claude, Gemini, Llama, Mistral) with graph databases (Neo4j, Azure Cosmos DB), relational and NoSQL stores, REST APIs, and cloud platforms (Azure, AWS, GCP) to deliver social-impact solutions for real users. Contribute to multilingual and multi-modal data pipelines — handling text, audio (ASR/TTS), images, and documents with pre-processing, translation, and structured extraction so that AI capabilities work across languages and input types.

Collaborating for Social Impact Work closely with data scientists, researchers, and partner NGO teams to help build open-source Digital Public Goods deployable at national scale — contributing clean, well-documented Python code and reusable modules. Communicate progress and technical trade-offs to both engineering peers and non-technical stakeholders; participate in cross-functional stand-ups, design reviews, and partner demos. Qualifications Bachelor’s or Master’s in Computer Science, AI/ML, Data Science, or a related engineering discipline — or equivalent demonstrable experience building AI-powered software products. 3 to 5 years of experience designing and building software, with at least one live end-to-end agentic or LLM-powered workload taken from concept to production deployment.

Comfortable working in cross-functional teams — able to discuss technical trade-offs with peers, translate requirements from domain experts, and communicate progress to non-technical stakeholders. Must-Have Skills Each bullet represents a skill area we will probe directly during screening. Shipped agentic AI: At least one live, end-to-end agentic workload in production — not a PoC, not a notebook.

A real system with real users. Agent framework depth: Hands-on experience with LangGraph, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or n8n — with a grasp of agentic patterns (ReAct, Plan-and-Execute, reflection loops, multi-agent coordination, tool/function calling, persistent memory). LLM and RAG expertise: Working knowledge of enterprise and open-source LLMs (GPT-4o, Claude, Gemini, Llama 3, Mistral, Phi); RAG engineering including chunking, embedding model selection (OpenAI Ada, Cohere, BGE), hybrid retrieval, re-ranking, and evaluation (RAGAS, TruLens, DeepEval); vector stores (Chroma, Weaviate, Pinecone, pgvector) and graph databases (Neo4j, Azure Cosmos DB).

Multi-modal and multilingual pipelines: Experience ingesting and processing text, images, documents (PDFs, HTML), and audio (ASR/TTS); familiarity with multilingual translation services (Azure Translator, IndicTrans, NLLB) for Indian language contexts. Cloud and MLOps fluency: Docker containerisation, CI/CD pipelines (GitHub Actions, Azure DevOps), model serving (FastAPI, Azure ML, BentoML), experiment tracking (MLflow, W&B), and observability (LangSmith, Phoenix) on at least one major cloud platform (Azure preferred; AWS or GCP acceptable). Python engineering: Strong Python (2+ years) — async/await, Pydantic, type hints, pytest, FastAPI/Flask, Git workflows; production-quality, testable, maintainable code, not just scripts or notebooks.

Preferred Qualifications Practical experience with Vision-Language Models (VLMs) such as GPT-4o Vision, LLaVA, or PaliGemma — and speech models (Whisper, Azure Speech, ElevenLabs) — used within live multi-modal agentic systems. Fluency with Azure OpenAI services (AOAI deployments, content filters, Managed Identity auth) and enterprise deployment patterns on Azure (Azure AI Studio, Azure Container Apps, Azure API Management for LLM gateway). Experience with fine-tuning or alignment techniques (LoRA, QLoRA, SFT, RLHF/DPO) on domain-specific datasets — especially for low-resource or Indian language models.

Familiarity with responsible AI practices: guardrails (Guardrails AI, NeMo Guardrails), bias detection, PII handling, and output validation — especially in social-impact deployments. Prior work delivering technology in social impact, public sector, or resource-constrained environments where reliability matters more than novelty. Contributions to open-source AI tooling, published technical blogs, or internal framework development — a GitHub profile showing real shipped work.

A4I Lab is committed to building AI systems that serve everyone. We actively encourage applications from candidates of all backgrounds, disciplines, and geographies.