AI Engineer

BT Group; Bengaluru; Hybrid; INR 2,500,000 - 4,500,000; Full time

Job summary

BT Group seeks an experienced AI/ML engineer to own production-grade AI features within Mind.AI. You will implement RAG pipelines, memory management, and guardrails, delivering scalable, observable services.

You will work independently from experiments to production, writing tests, benchmarking, and shipping to the Lead AI Engineer with a strong quality bar. You will collaborate on retrieval, safety, and platform engineering across data stores (PostgreSQL, Redis, Neo4j) and vector databases,

Qualifications

• Experience with distributed systems and microservices architecture.
• Knowledge of event-driven systems using Kafka and NATS.
• REST APIs, real-time communication, API security, JWT, rate limiting, and resilience patterns.
• Experience building AI agents using LangChain and LangGraph.
• Single-agent and multi-agent workflows, including ReAct, Planning, and Tool-Use patterns.
• Strong understanding of prompt engineering, memory, and multi-LLM integration.
• Experience with Python, FastAPI, and AI/ML stacks (PyTorch, SpaCy, Transformers).
• Knowledge of retrieval, embeddings, vector databases, and Elasticsearch (BM25).
• Experience with AI evaluation frameworks (RAGAS, DeepEval) and safety practices.
• Experience with PostgreSQL, pgvector, Redis, Kafka, and Neo4j knowledge graphs.

Responsibilities

• Build and own RAG pipelines for knowledge retrieval at scale.
• Design document ingestion, embedding, and retrieval workflows.
• Implement hybrid search, re-ranking, and citation-based responses.
• Develop guardrails for safety, data protection, and content redaction.
• Create evaluation and benchmarking frameworks; iterate to improve accuracy.
• Design scalable AI services and APIs with observability and reliability.
• Develop autonomous agent/multi-agent orchestration and memory capabilities.

Skills

Distributed systems

Kafka

REST APIs

JWT

Rate limiting

Resilience patterns

Single-agent workflows

Multi-agent workflows

ReAct

Planning

Tool-Use patterns

Prompt engineering

Memory management

Multi-LLM integration

Python

FastAPI

Pydantic

SpaCy

Sentence Transformers

PyTorch

Hugging Face

ONNX

LoRA/QLoRA

vLLM

RAGAS

DeepEval

Benchmarking

LLM-as-a-Judge

Human-in-the-loop

PostgreSQL

pgvector

Redis

Neo4j knowledge graphs

Elasticsearch

RAG design

Document chunking

Embeddings

Vector databases

Hybrid search

Cross-encoder re-ranking

LangChain

LangGraph

Tools

LangChain

LangGraph

Job description

About the role

You will build and own scoped AI service features within the Mind.AI platform. You work within the architecture set by the Lead AI Engineer, take feature specifications and deliver production-quality implementations: a chunking strategy module, a guardrail model integration, an embedding pipeline stage, a RAGAS metric computation job. You are expected to work independently within scope - take ownership, write tests, benchmark your work, and ship to the Lead's quality bar. You have built ML or AI features in production before. You know that a model that scores well in a notebook evaluation is not done - it needs to be packaged, served, monitored, and maintained. You are comfortable with the full lifecycle from experiment to production deployment.

Role & responsibilities

RAG & Knowledge Retrieval

• Build and optimise enterprise-grade RAG pipelines for accurate knowledge retrieval
• Develop document ingestion, indexing, embedding, and retrieval workflows
• Implement hybrid search, re-ranking, and citation-based response generation
• Improve retrieval quality, relevance, and scalability across large knowledge bases

AI Safety & Guardrails

• Implement PII detection, data protection, and content redaction controls
• Integrate prompt injection, toxicity, and misuse detection mechanisms
• Build AI guardrails to ensure safe, compliant, and trustworthy responses
• Develop automated response quality and faithfulness evaluation frameworks

Memory & Knowledge Management

• Design and implement long-term AI memory frameworks
• Build user, agent, and organisational knowledge retention capabilities
• Develop knowledge graph and graph-based retrieval solutions
• Optimise context management through intelligent summarisation and memory retrieval

Evaluation & Optimisation

• Define and implement AI evaluation metrics and testing frameworks
• Create and maintain golden datasets for model validation
• Conduct experiments to improve retrieval, reasoning, and response quality
• Drive continuous performance optimisation through benchmarking and analytics

Platform Engineering

• Design scalable, production-ready AI services and APIs
• Optimise latency, throughput, reliability, and cost of AI workloads
• Build monitoring, observability, and auditability for AI systems
• Collaborate with platform, data, and product teams to deliver enterprise AI solutions

Agentic AI & Multi-Agent Systems

• Design and develop autonomous AI agents and multi-agent workflows
• Build orchestration frameworks for planning, reasoning, and task execution
• Implement agent memory, tool calling, and decision-making capabilities
• Enable enterprise-scale deployment, governance, and monitoring of agentic solutions

Skills and Experience Required

1. Systems Architecture

• Experience with distributed systems and microservices architecture.
• Knowledge of event-driven systems using Kafka and NATS.
• Skilled in REST APIs, real-time communication, API security, JWT, rate limiting, and resilience patterns.

2. LLM Orchestration & Agentic AI

• Experience building AI agents using LangChain and LangGraph.
• Skilled in single-agent and multi-agent workflows, including ReAct, Planning, and Tool-Use patterns.
• Strong understanding of prompt engineering, context management, memory, and multi-LLM integration.

3. Python & AI/ML Stack

• Strong programming skills in Python, FastAPI, and Pydantic.
• Experience with NLP and AI frameworks including SpaCy, Sentence Transformers, PyTorch, and Hugging Face.
• Knowledge of ONNX, LoRA/QLoRA fine-tuning, vLLM, LangChain, LangGraph, and RAGAS.

4. Retrieval & Search

• Experience designing Retrieval-Augmented Generation (RAG) solutions.
• Skilled in document chunking, embeddings, vector databases, and Elasticsearch (BM25).
• Knowledge of hybrid search and cross-encoder re-ranking techniques.

5. Evaluation, Safety & Responsible AI

• Experience with AI evaluation frameworks such as RAGAS and DeepEval.
• Skilled in benchmarking, LLM-as-a-Judge, and human-in-the-loop evaluation.
• Knowledge of AI safety, prompt injection prevention, red teaming, and industry safety benchmarks.

6. Data Platforms

• Experience with PostgreSQL, pgvector, and Redis.
• Knowledge of Kafka for event streaming and data processing.
• Skilled in building and working with Neo4j knowledge graphs.