Artificial Intelligence Engineer

Bain; Gurugram District; Hybrid; INR 1,500,000 - 2,100,000; Full time

Job summary

Bain is seeking a data/AI-focused engineer to build scalable data pipelines and deploy ML models into production. You will design backend APIs, integrate agentic AI capabilities, and develop lightweight front-end interfaces for business users.

You will collaborate with data scientists, work with cloud data platforms, and implement robust software engineering practices to deliver data-driven solutions across the stack.

Qualifications

• Strong programming skills in Python and SQL.
• ,
• Hands-on experience with building data pipelines and backend services.
• Experience with ML model integration and deployment in production.
• Familiarity with vector databases (pgvector, Pinecone).
• Experience designing agentic AI systems using frameworks like LangGraph, LangChain including tool calling and memory management.
• Exposure to MLOps practices like model versioning, drift monitoring, and experiment tracking.
• Experience developing backend APIs using FastAPI, Flask, or Django.
• Frontend exposure with ReactJS/NextJS/AngularJS/Dash to build dashboards.
• Experience with cloud platforms (AWS/Azure/GCP) and data architectures.
• Familiarity with CI/CD, Docker, and Git.
• Understanding data modeling, ETL, scalable design; experience with Neo4J and graph databases.
• Ability to work across stack from data pipelines to APIs to front-end interfaces.
• Nice-to-have: Excel/PowerPoint for business storytelling.
• Nice-to-have: Consulting/analytics or Consumer Products domain exposure.

Responsibilities

• Build and maintain scalable data pipelines and data processing workflows to support analytics and ML use cases.
• Develop and deploy ML models into production systems with reliability and scalability.
• Develop and deploy agentic AI applications including conversational chatbots and autonomous task-execution agents.
• Design and implement backend APIs to serve data products and ML outputs to downstream apps.
• Develop lightweight frontend applications, dashboards, or interfaces for business users to interact with data and models.
• Collaborate with data scientists to productionize models and integrate them end-to-end.
• Work with large-scale datasets across cloud platforms and warehouses (Snowflake, Azure Synapse).
• Implement best practices in software engineering: modular code, testing, version control.
• Translate business requirements into technical solutions and support stakeholders with data-driven tools.

Skills

Python

SQL

Data pipelines

Backend services

ML model integration

ML deployment

LangGraph

LangChain

MLOps

CI/CD

Tools

FastAPI

Flask

Django

ReactJS

NextJS

AngularJS

Dash

Docker

Git

Neo4J

Pinecone

pgvector

LangGraph

LangChain

MLflow

Job description

Role & responsibilities

Strong programming skills in Python and SQL, with experience building data pipelines and backend services

. Hands-on experience with machine learning model integration and deployment in production environments.

Familiarity with vector databases (pgvector, Pinecone)

Experience designing and implementing agentic AI systems using frameworks such as LangGraph, LangChain - including tool/function calling, memory management, and familiarity with frameworks such as MCP, A2A

Exposure to MLOps practices like model versioning, drift monitoring, and experiment tracking (MLflow)

Experience developing backend APIs using frameworks such as FastAPI, Flask, or Django.

Exposure to frontend technologies (e.g., ReactJS, NextJS, AngularJS, Dash, or similar) to build user-facing applications and dashboards.

Experience working with cloud platforms (AWS/Azure/GCP) and modern data architectures (data lakes, warehouses).

Familiarity with CI/CD pipelines, Docker, and version control (Git).

Understanding of data modeling, ETL processes, and scalable system design and experienced in Neo4J, Graph Databases and creating Business Ontology.

Ability to work across the stackfrom data pipelines to APIs to front-end interfaces.

Good-to-have: Proficiency in Excel and PowerPoint with ability to support business communication and storytelling.

Good-to-have: Prior exposure to consulting, analytics use cases, or Consumer Products domain.

Preferred candidate profile

Build and maintain scalable data pipelines and data processing workflows to support analytics and machine learning use cases.

Develop and deploy machine learning models into production systems, ensuring reliability and scalability.

/ Develop and deploy agentic AI applications including conversational chatbots, autonomous task-execution agents, and multi-agent workflows

Design and implement backend APIs to serve data products and ML outputs to downstream applications

. Develop lightweight frontend applications, dashboards, or interfaces to enable business users to interact with data and models.

Collaborate with data scientists to productionize models and integrate them into end-to-end solutions.

Work with large-scale datasets across cloud-based data platforms and warehouses (e.g., Snowflake, Azure Synapse).

Implement best practices in software engineering, including modular code design, testing, and version control.

Utilize containerization (Docker) and CI/CD pipelines for efficient deployment and scaling of applications.

Optimize performance of data pipelines, APIs, and user-facing applications

. Translate business requirements into technical solutions and support stakeholders with data-driven tools and insights.