AI Engineer (RAG-based Chatbot)

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, and vector databases . The ideal candidate will work on building real-time AI systems such as chatbots, semantic search, recommendation systems, document intelligence, and autonomous AI workflows.

You will be responsible for the end-to-end lifecycle of AI pipelines that include data ingestion, embedding generation, vector storage, retrieval, and LLM-based response generation or automated actions.

Experience: 57 Years

Location: Bangalore

Employment Type: Full-Time

Key Responsibilities

Design and implement embedding pipelines for text, documents, images, or 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.

Fine-tune prompt strategies and system prompts for optimal LLM performance.

Integrate LLMs such as:

OpenAI, Azure OpenAI, Anthropic, Google Gemini, Meta LLaMA, Mistral, etc.

Build APIs and microservices for AI features using:

Python / Java / Node.js / Spring Boot / FastAPI

Implement similarity scoring, ranking, filtering, and metadata-based retrieval .

Monitor, optimize, and scale vector search performance.

Handle LLM cost optimization, latency reduction, and caching strategies .

Implement AI safety, hallucination reduction, and response validation mechanisms .

Work closely with product, frontend, and data teams.

Deploy AI workloads on cloud platforms (AWS, Azure, GCP, OCI) .

Maintain CI/CD pipelines for AI services.

Required Skills & Qualifications

Mandatory Core AI & LLM Skills

Strong understanding of:

Embeddings

Vector similarity search

Cosine similarity, dot product, ANN indexing

Hands-on experience with:

LangChain / LlamaIndex / Semantic Kernel / Spring AI

Experience with RAG architectures

Experience with at least one Vector Database

Proficient in prompt engineering and LLM orchestration

Programming & Backend

Strong in Python / Java / JavaScript / TypeScript

API development with FastAPI, Flask, Spring Boot, or Node.js

Strong understanding of REST APIs, background jobs, async processing

Data & Storage

Experience with:

PostgreSQL, MySQL, MongoDB

Object storage (S3, OCI, Azure Blob)

Data preprocessing, chunking, tokenization strategies

Cloud & DevOps (Good to Have)

Docker & Kubernetes

CI/CD pipelines (Jenkins, GitHub Actions, GitLab, Bitbucket)

Monitoring with Prometheus, Grafana, OpenTelemetry

Good to Have (Preferred Skills)

Experience with Agentic AI frameworks

Knowledge of Tool Calling / Function Calling

Experience with Speech-to-Text and Vision models

Fine-tuning or LoRA experience

Knowledge of security & data privacy for AI systems

Experience building autonomous AI workflows

Use Cases You Will Work On

AI chatbots for customer support

Semantic document search

Knowledge-base Q&A systems

Recommendation engines

Automated ticket triaging

AI assistants for developers or operations

Intelligent workflow automation