Job Title: 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+ Years overall, with over 1 year experience in building Agentic AI

Location:

Bangalore

Employment Type:

Full-Time/Contractual

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, We aviate, 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 & Qualification :

Mandatory Core AI, LLM & Agentic Skills 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:
- Lang Chain / Llama Index / Semantic Kernel / Spring AI
- Experience building multi-agent systems and agent orchestration pipelines
- Experience building MCP servers for tool and context exposure




- Experience with Lang Graph / 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:

- Robust proficiency in Python / Java / JavaScript / TypeScript
- API development using Fast API, 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, Open Telemetry
- 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:

- 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

📌 Sr AI Engineer (Bengaluru)
🏢 Workfall
📍 Bengaluru