Artificial Intelligence Engineer

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.