Senior AI ML Engineer

Overview

As a Senior AI/ML Engineer – Supply Chain AI & Intelligent Automation, design, develop, and deploy Artificial Intelligence, Machine Learning, and Generative AI solutions that improve Supply Chain functions. Build scalable, production-ready AI applications that enable intelligent decision-making, automation, and operational efficiency.

What you'll do

• Design, develop, and deploy scalable Machine Learning models and AI solutions to solve complex Supply Chain business challenges.
• Build end-to-end ML pipelines for forecasting, optimization, predictive analytics, anomaly detection, and intelligent automation.
• Design, build, test, and deploy AI Agents and multi-agent systems using frameworks such as Lang Graph, Lang Chain, Auto Gen, Crew AI, or similar technologies.
• Develop intelligent workflows that leverage LLMs, tool integration, memory, and orchestration to automate business processes and improve operational decision-making.
• Develop robust data ingestion, transformation, and feature engineering pipelines for structured, semi-structured, and unstructured enterprise data.
• Integrate enterprise knowledge repositories, knowledge graphs, vector databases, and intelligent document processing solutions to enable contextual AI insights, enhance retrieval capabilities, and support scalable Machine Learning and Generative AI applications.
• Deploy and manage AI and Machine Learning applications on AWS and Microsoft Azure.
• Build and maintain MLOps and LLMOps pipelines, including model versioning, CI/CD, automated deployment, monitoring, and retraining.
• Evaluate, monitor, and optimize Machine Learning models and Large Language Models using appropriate performance metrics.
• Improve model accuracy, reduce inference latency, optimize cloud resource utilization, and implement responsible AI practices.
• Partner with Supply Chain stakeholders, Data Scientists, Software Engineers, Cloud Architects, Product Managers, and Digital Transformation teams to translate business requirements into scalable AI-ML solutions.
• Establish engineering standards, contribute to architecture and code reviews, and create technical documentation.

What you'll need

• Bachelor’s degree in computer science, Software Engineering, AI, or related field and 7+ years of professional experience in Machine Learning, Artificial Intelligence, Data Science, or AI Engineering, or a master’s degree with relevant industry experience and 5+ years of experience.
• Strong hands-on expertise in Python.
• Proven experience designing, developing, deploying, and optimizing scalable Machine Learning and AI solutions in production environments.
• Experience with predictive modelling, forecasting, optimization, feature engineering, model evaluation, monitoring, and lifecycle management using cloud platforms such as Azure, Data Bricks and AWS.
• Experience building and deploying Generative AI applications and Agentic AI workflows in production using frameworks such as Lang Graph, Lang Chain, Auto Gen, Crew AI, or similar technologies production grade in AWS / Azure cloud.
• Practical experience with Large Language Models, Prompt Engineering, RAG, AI evaluation techniques, and responsible AI practices.
• Strong understanding of system design patterns, microservices architecture, APIs, containerization including Docker, Kubernetes, and infrastructure automation.
• Experience implementing AI observability and evaluation using tools such as Azure AI Foundry, Azure Monitor, Azure ML Monitoring, AWS CloudWatch, ML flow, Lang Smith, Prometheus, Grafana, or Open Telemetry to monitor AI quality, latency, reliability, cost, and operational performance.
• Experience working with enterprise data platforms, data pipelines, SQL, and distributed data processing frameworks to support AI/ML solutions.

Nice to have

• Hands-on experience developing AI/ML solutions for Supply Chain, Healthcare, or other enterprise domains.
• Experience implementing MLOps and LLMOps practices, including model versioning, CI/CD pipelines, automated deployment, monitoring, observability, and AI lifecycle management.
• Strong understanding of system design patterns, microservices architecture, APIs, containerization including Docker, Kubernetes, and infrastructure automation.
• Familiarity with AI governance, model explainability, data security, privacy, and Responsible AI practices.

Details

• Location: Bangalore.
• Work mode: Hybrid.
• Travel: 10-20%.