We partner closely with the brightest minds in healthcare to ensure that every solution we create melds the latest technology with compassion and empathy. Because at Solventum, we never stop solving for you.
The Impact You’ll Make in this Role
As an Associate AI/ML Engineer — Supply Chain AI & Intelligent Automation, you will support the design, development, and deployment of Artificial Intelligence (AI), Machine Learning (ML), and Generative AI solutions that improve Supply Chain across functions. You will help build and maintain scalable, production-ready AI applications that enable intelligent decision-making, automation, and operational efficiency.
In this role, you will assist in developing Machine Learning models, AI Agents, and cloud-native AI solutions using AWS and Azure to help solve supply chain challenges, working under the guidance of senior engineers and data scientists.
You will collaborate with Supply Chain stakeholders, Senior/Lead Data Scientists, Cloud Architects, and Analytics Teams to help translate business requirements into reliable, scalable AI solutions that deliver measurable business value, while continuing to build your technical expertise.
Key Responsibilities
Machine Learning Solution Development: Assist in designing, developing, and deploying Machine Learning models and AI solutions to help solve Supply Chain business challenges. Contribute to end-to-end ML pipelines for forecasting, optimization, predictive analytics, anomaly detection, and intelligent automation, under the guidance of senior team members, helping ensure models are production-ready, reliable, and scalable.
Agentic AI & Generative AI Engineering: Support the design, build, testing, and deployment of AI Agents and multi-agent systems using frameworks such as Lang Graph, Lang Chain, Auto Gen, Crew AI, or similar technologies. Help 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: Help develop data ingestion, transformation, and feature engineering pipelines to process structured, semi-structured, and unstructured enterprise data. Support integration of enterprise knowledge repositories, knowledge graphs, vector databases, and intelligent document processing solutions to enable contextual AI insights and support scalable Machine Learning and Generative AI applications.
Cloud AI Deployment & MLOps: Assist in deploying and maintaining AI and Machine Learning applications on AWS and Microsoft Azure. Support MLOps and LLMOps pipelines, including model versioning, CI/CD, automated deployment, monitoring, and retraining, to help ensure scalable, secure, and high-performing production AI systems.
Model Performance & AI Optimization: Help evaluate and monitor Machine Learning models and Large Language Models (LLMs) using appropriate performance metrics. Support efforts to improve model accuracy, reduce inference latency, optimize cloud resource utilization, and implement responsible AI practices under senior guidance.
Technical Collaboration & Engineering Excellence: Partner with Supply Chain stakeholders, Data Scientists, Software Engineers, Cloud Architects, Product Managers, and Digital Transformation teams to help translate business requirements into AI-ML solutions. Participate in code reviews, follow engineering standards, and help create technical documentation.
Your Skills and Expertise
Minimum Qualifications
Bachelor’s degree in computer science, Software Engineering, AI, or related field and 2 to 4 years of professional experience in Machine Learning, Artificial Intelligence, Data Science, or Ai Engineering or a master’s degree with relevant industry experience and 2 to 3 years of experience with internship experience.
Hands-on 3+ years of experience with Python and foundational knowledge of designing, developing, and supporting Machine Learning and AI solutions, including exposure to predictive modelling, forecasting, feature engineering, and model evaluation, ideally using cloud platforms such as Azure, Databricks, or AWS.
Exposure to or coursework/project experience with Generative AI applications and Agentic AI workflows using frameworks such as Lang Graph, Lang Chain, Auto Gen, Crew AI, or similar technologies.
Foundational understanding of Large Language Models (LLMs), Prompt Engineering, RAG, and AI evaluation techniques, with a willingness to deepen expertise in responsible AI practices.
Basic understanding of system design patterns, microservices architecture, APIs, containerization (Docker), Kubernetes, and infrastructure automation.
Familiarity with, or eagerness to learn, AI observability and evaluation tools such as Azure AI Foundry, Azure Monitor, Azure ML Monitoring, AWS CloudWatch, MLflow, Lang Smith, Prometheus, Grafana, or Open Telemetry.
Exposure to enterprise data platforms, data pipelines, SQL, and distributed data processing frameworks to support AI/ML solutions.
Preferred Qualifications
Academic, internship, or project experience developing AI/ML solutions for Supply Chain, Retail, Healthcare, or other enterprise domains.
Location: Bangalore (Hybrid)
Travel: 40% commuting to office
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