Job Title

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+ YearsLocation: BangaloreEmployment Type: Full-TimeKey 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:ChatbotsQ&A systemsRecommendation enginesAI agents and automation workflowsImplement 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 / FastAPIImplement 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 SkillsStrong understanding of:EmbeddingsVector similarity searchCosine similarity, dot product, ANN indexingRAG architecturesHands-on experience with:LangChain / LlamaIndex / Semantic Kernel / Spring AIExperience building multi-agent systems and agent orchestration pipelinesExperience building MCP servers for tool and context exposureExperience with LangGraph / Strands or similar agent workflow orchestration toolsExperience implementing A2A (Agent-to-Agent) communication patternsProficient in prompt engineering, memory management, and LLM orchestrationExperience with at least one Vector Database2) Programming & Backend:Strong proficiency in Python / Java / JavaScript / TypeScriptAPI development using FastAPI, Flask, Spring Boot, or Node.jsStrong understanding of REST APIs, async processing, event-driven architecturesExperience building microservices for AI agents3) Data & Storage:Experience with:PostgreSQL, MySQL, MongoDBObject storage (S3, OCI, Azure Blob)Data preprocessing, chunking strategies, tokenization optimizationKnowledge of metadata filtering and hybrid search4) Cloud & DevOps (Good to Have):Docker & KubernetesCI/CD pipelines (Jenkins, GitHub Actions, GitLab, Bitbucket)Monitoring with Prometheus, Grafana, OpenTelemetryExperience deploying scalable AI inference pipelinesPreferred Skills:Deep experience with Agentic AI frameworksKnowledge of Tool Calling / Function CallingExperience with workflow engines and orchestration graphsExperience with Speech-to-Text, Vision modelsFine-tuning, LoRA, PEFT experienceKnowledge of AI security, governance & data privacyExperience building autonomous AI systems with memory + toolsExperience designing distributed agent architecturesUse Cases You Will Work On:AI chatbots for customer supportSemantic document searchKnowledge-base Q&A systemsMulti-agent workflow automationIntelligent AI copilotsAutomated ticket triagingAI assistants for developers and operationsCollaborative agent systems using A2A protocolsMCP-based tool-integrated AI systems