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: 4+ Years 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: 1) 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 2) 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 3) 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 4) 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 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