Job Title : AI Engineer (RAG & Multi-Agent Systems)

Employment Type : Full-time / WFO 5 days, C2H/ Permanent Remote

Description :

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 / for Contract Remote

Employment Type : Full-Time / Contract - Permanent Remote work

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 :

1. Chatbots

2. Q&A systems

3. Recommendation engines

4. 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 :

1. Embeddings

2. Vector similarity search

3. Cosine similarity, dot product, ANN indexing

4. 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 :

1. PostgreSQL, MySQL, MongoDB

2. 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