Sr AI ML Engineer

Job Summary

Role: Senior AI-ML Engineer / Data Scientist,

As a Senior AI/ML Engineer - Supply Chain AI Intelligent Automation, you will design, develop, and deploy Artificial Intelligence (AI), Machine Learning (ML), and Generative AI solutions that improve Supply Chain across functions. You will build scalable, production-ready AI applications that enable intelligent decision-making, automation, and operational efficiency.

In this role, you will develop Machine Learning models, AI Agents, and cloud-native AI solutions using AWS and Azure to solve complex supply chain challenges.

You will collaborate with Supply Chain stakeholders, Lead Data Scientists, Cloud Architects, and Analytics Teams to translate business requirements into reliable, scalable AI solutions that deliver measurable business value.

Key Responsibilities

Machine Learning Solution Development

• 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, ensuring models are production-ready, reliable, and scalable.

  Agentic AI Generative AI Engineering

• 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.

  Enterprise Data Engineering AI Integration

• Develop robust data ingestion, transformation, and feature engineering pipelines to process 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.

  Cloud AI Deployment MLOps

• 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 to ensure scalable, secure, and high-performing production AI systems.

  Model Performance AI Optimization

• Evaluate, monitor, and optimize Machine Learning models and Large Language Models (LLMs) using appropriate performance metrics. Improve model accuracy, reduce inference latency, optimize cloud resource utilization, and implement responsible AI practices to ensure reliable and cost-effective AI solutions.

  Technical Collaboration Engineering Excellence

• 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, create technical documentation.

  Your Skills and Expertise

• 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 with proven experience designing, developing, deploying, and optimizing scalable Machine Learning and AI solutions in production environments, including 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 (LLMs), Prompt Engineering, RAG, AI evaluation techniques, and responsible AI practices.

• Strong understanding of system design patterns, microservices architecture, APIs, containerization (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.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.