AI Engineer (RAG & Multi-Agent Systems)

We are looking for an experienced AI/LLM Engineer to design, build, and maintain intelligent applications powered by Large Language Models (LLMs), embeddings, similarity search, vector databases, and multi-agent architectures.

The ideal candidate will build real-time AI systems such as chatbots, semantic search engines, recommendation systems, document intelligence platforms, MCP servers, and autonomous multi-agent workflows capable of tool usage and inter-agent communication.

You will own the end-to-end lifecycle of AI pipelines including data ingestion, embedding generation, vector storage, retrieval, LLM response orchestration, tool invocation, agent communication, and automated decision workflows.

Experience:

5-10 Years overall, with over 1 year experience in building Agentic AI Location:

Bangalore Employment Type:

Full-Time

Key Responsibilities

Design and implement embedding pipelines for text, documents, images, and structured data. Build and optimize semantic search and similarity search systems using vector databases. Integrate and manage vector databases such as: Pinecone, Weaviate, Milvus, FAISS, Chroma, OpenSearch Vector Engine, etc. Develop LLM-powered applications for: Chatbots Q&A systems Recommendation engines AI agents and automation workflows Implement RAG (Retrieval Augmented Generation) pipelines with hybrid retrieval and reranking. Design and develop multi-agent architectures (planner-executor, supervisor-worker, tool-using agents). Build and deploy MCP (Model Context Protocol) servers to expose tools, memory, and external systems to LLM agents. Develop structured agentic workflows using frameworks like LangGraph, Strands, or similar orchestration engines. Implement multi-agent communication using A2A (Agent-to-Agent) protocols for collaborative reasoning and task execution. Design tool-calling pipelines and function-calling integrations. Fine-tune prompt strategies, memory handling, and system prompts for optimal LLM performance. Integrate LLM providers such as: OpenAI, Azure OpenAI, Anthropic, Google Gemini, Meta LLaMA, Mistral, etc. Build APIs and microservices for AI systems using: Python / Java / Node.js / Spring Boot / FastAPI Implement similarity scoring, ranking, filtering, and metadata-based retrieval. Monitor, optimize, and scale vector search performance. Optimize LLM cost, latency, caching, and response validation strategies. Implement AI safety mechanisms, hallucination reduction, guardrails, and evaluation pipelines. Work closely with product, frontend, and data teams. Deploy AI workloads on AWS, Azure, GCP, or OCI. Maintain CI/CD pipelines for AI services.

Required Skills & Qualifications

Mandatory Core AI, LLM & Agentic Skills

Strong understanding of: Embeddings Vector similarity search Cosine similarity, dot product, ANN indexing RAG architectures Hands-on experience with: LangChain / LlamaIndex / Semantic Kernel / Spring AI Experience building multi-agent systems and agent orchestration pipelines Experience building MCP servers for tool and context exposure Experience with LangGraph / Strands or similar agent workflow orchestration tools Experience implementing A2A (Agent-to-Agent) communication patterns Proficient in prompt engineering, memory management, and LLM orchestration Experience with at least one Vector Database

Programming & Backend

Strong proficiency in Python / Java / JavaScript / TypeScript API development using FastAPI, Flask, Spring Boot, or Node.js Strong understanding of REST APIs, async processing, event-driven architectures Experience building microservices for AI agents.

Data & Storage

Experience with: PostgreSQL, MySQL, MongoDB Object storage (S3, OCI, Azure Blob) Data preprocessing, chunking strategies, tokenization optimization Knowledge of metadata filtering and hybrid search Cloud & DevOps (Good to Have)

Docker & Kubernetes CI/CD pipelines (Jenkins, GitHub Actions, GitLab, Bitbucket) Monitoring with Prometheus, Grafana, OpenTelemetry Experience deploying scalable AI inference pipelines

Good to Have (Preferred Skills)

Deep experience with Agentic AI frameworks Knowledge of Tool Calling / Function Calling Experience with workflow engines and orchestration graphs Experience with Speech-to-Text, Vision models Fine-tuning, LoRA, PEFT experience Knowledge of AI security, governance & data privacy Experience building autonomous AI systems with memory + tools Experience designing distributed agent architectures

Use Cases You Will Work On

AI chatbots for customer support Semantic document search Knowledge-base Q&A systems Multi-agent workflow automation Intelligent AI copilots Automated ticket triaging AI assistants for developers and operations Collaborative agent systems using A2A protocols MCP-based tool-integrated AI systems