Senior AI/ML Engineer

Chapter247 Infotech; Indore District; On-site; INR 4,000,000 - 8,000,000; Full time

Job summary

Chapter247 Infotech is seeking a Senior AI/ML Engineer to own the end-to-end lifecycle of AI-powered applications. The role emphasizes translating business needs into scalable AI-driven solutions, guiding design and deployment, and driving best practices across the engineering team.

The successful candidate will shape architectural decisions, mentor peers, implement secure tool execution, and optimize performance for large-scale AI systems.

Qualifications

• Strong Python and backend framework experience with FastAPI/Django/Flask.
• Hands-on AI-powered app development using LLMs, RAG, OCR, and agentic AI.
• Knowledge of AI safety, prompt/version management, and secure tool execution.
• Experience with NLP, embeddings, and vector databases like Pinecone/FAISS.
• Experience with cloud services (AWS/GCP/Azure) and third-party APIs.

Responsibilities

• Analyse business requirements and design scalable backend systems, AI workflows, architectures, and implementation approaches.
• Develop, maintain, and optimize AI-powered applications using LLMs, RAG pipelines, OCR systems, and Agentic AI frameworks.
• Research, evaluate, benchmark, and implement AI/ML models, prompts, and retrieval strategies.
• Conduct experiments and PoCs for new AI capabilities and technologies.
• Implement secure, production-ready AI systems with guardrails, observability, and performance optimization.
• Troubleshoot complex issues, identify root causes, and provide scalable solutions.
• Contribute to architecture decisions, coding standards, and testing.
• Integrate third-party APIs, cloud platforms, and external AI providers.
• Communicate technical solutions and project updates with stakeholders.
• Use AI-assisted tooling to improve productivity and workflows.
• Mentor junior developers with technical guidance and design input.

Skills

Client interaction

Architecture decisions

Team mentoring

Engineering best practices

Problem understanding

Design and development

Tools

Python

FastAPI

Django REST

Flask RESTful

Pydantic

SQLAlchemy

Async programming

REST API design

Authentication

Backend architecture patterns

Generative AI

Agentic AI

LLMs

Prompt Engineering

RAG

OCR

Tool calling

Memory/context management

Multi-step reasoning

Autonomous workflows

AI safety

Prompt/version management

NLP

Embeddings

TensorFlow

PyTorch

Hugging Face

Vector databases

GA/FAISS

Temporal

Kafka

RabbitMQ

SQS

gRPC

MongoDB

PostgreSQL

MySQL

Firebase

Redis

AWS

GCP

Azure

Git

CI/CD

Docker

Terraform

Pytest

Container deployment

OWASP/API security

Performance optimization

Cursor

Copilot

ChatGPT

Claude

JavaScript

HTML

CSS

React

Job description

We are looking for a Senior AI/ML Engineer who can take ownership beyond implementation and contribute across the complete software development lifecycle.

The ideal candidate should be capable of understanding business problems, interacting with clients and stakeholders, translating requirements into scalable technical solutions, and driving them through design, development, deployment, and continuous improvement.

In addition to strong AI and backend engineering expertise, we are looking for someone who can contribute to architectural decisions, guide team members, establish engineering best practices, and help us scale a high-performing AI engineering team.

Skills

• Strong hands‑on experience in Python and backend frameworks such as FastAPI, Django REST, Flask RESTful, along with Pydantic, SQLAlchemy, async programming, scalable REST API design, authentication, and backend architecture patterns.
• Practical experience building AI-powered applications using Generative AI, Agentic AI, LLMs, Prompt Engineering, RAG, OCR, AI agents, tool calling, memory/context management, multi‑step reasoning, and autonomous workflows.
• Good understanding of AI safety and security practices including prompt injection prevention, guardrails, response validation, content filtering, hallucination mitigation, secure tool execution, AI observability, and prompt/version management.
• Experience with NLP, embeddings, semantic search, model training, deep learning frameworks such as TensorFlow, PyTorch, Hugging Face, and vector databases like Pinecone, Weaviate, Qdrant, ChromaDB, or FAISS.
• Experience with workflow orchestration and distributed systems using tools such as Temporal, LangGraph, Kafka, RabbitMQ, SQS, and communication protocols like gRPC.
• Hands‑on experience with databases including MongoDB, PostgreSQL/MySQL, Firebase, Redis, and scalable data architecture.
• Experience integrating third‑party APIs, cloud services (AWS/GCP/Azure), payment/email/SMS providers, and external AI services.
• Proficient with Git, CI/CD, Docker, Terraform, testing frameworks like Pytest, and containerized deployment practices.
• Strong understanding of secure coding practices, OWASP/API security standards, and performance optimization for scalable AI systems.
• Experience using AI‑assisted development tools such as Cursor, Copilot, ChatGPT, Claude, or similar tools.
• Good to have experience with frontend technologies such as JavaScript, HTML5, CSS, and React.

Roles & Responsibilities

• Analyse business requirements and design scalable backend systems, AI workflows, architectures, and implementation approaches.
• Develop, maintain, and optimize AI-powered applications using LLMs, RAG pipelines, OCR systems, and Agentic AI frameworks.
• Research, evaluate, benchmark, and implement suitable AI/ML models, embeddings, prompts, retrieval strategies, and frameworks based on business requirements.
• Conduct experiments, benchmarking, and PoC implementations for new AI capabilities and technologies.
• Implement secure, reliable, and production‑ready AI systems with proper guardrails, validation mechanisms, observability, and performance optimization.
• Troubleshoot complex technical issues, identify root causes, and provide scalable long‑term solutions.
• Contribute to architecture decisions, task estimation, technical discussions, coding standards, testing, and development best practices.
• Integrate third‑party APIs, cloud platforms, and external AI providers into the product ecosystem.
• Communicate technical solutions and project updates effectively with technical and non‑technical stakeholders.
• Use AI‑assisted engineering tools to improve development productivity and engineering workflows.
• Mentor junior developers by providing technical guidance, design recommendations, and implementation support.