1 day ago
Bengaluru, IndiaSenior
Responsibilities
- Design, develop, and deploy scalable machine learning models and end-to-end ML pipelines for supply chain forecasting, optimization, predictive analytics, anomaly detection, and intelligent automation.
- Build and deploy AI agents and multi-agent systems using LLMs, tool integration, memory, orchestration, and frameworks such as LangGraph, LangChain, AutoGen, and CrewAI.
- Develop data ingestion, transformation, feature engineering, enterprise data integration, knowledge graph, vector database, and intelligent document processing solutions.
- Deploy and manage AI and machine learning applications on AWS and Azure while building MLOps and LLMOps pipelines for versioning, automated deployment, monitoring, and retraining.
- Evaluate and optimize machine learning models and LLMs for accuracy, inference latency, cloud resource utilization, reliability, and cost.
- Implement AI observability and evaluation capabilities and contribute to system architecture, engineering standards, code reviews, and technical documentation.
- Partner with supply chain stakeholders, data scientists, software engineers, cloud architects, product managers, and digital transformation teams to deliver scalable AI/ML solutions.
Requirements
- Bachelor’s degree in computer science, software engineering, AI, or a related field with 7+ years of professional experience, or a master’s degree with relevant industry experience and 5+ years of experience.
- Strong hands-on Python experience developing, deploying, and optimizing production machine learning and AI solutions, including predictive modeling, forecasting, optimization, feature engineering, model evaluation, monitoring, and lifecycle management.
- Experience building and deploying production-grade generative AI applications and agentic AI workflows in AWS or Azure using frameworks such as LangGraph, LangChain, AutoGen, or CrewAI.
- Practical experience with large language models, prompt engineering, retrieval-augmented generation, AI evaluation techniques, and responsible AI practices.
- Understanding of system design patterns, microservices architecture, APIs, Docker, Kubernetes, and infrastructure automation.
- Experience with AI observability and evaluation tools such as Azure AI Foundry, Azure Monitor, Azure ML Monitoring, AWS CloudWatch, MLflow, LangSmith, Prometheus, Grafana, or OpenTelemetry.
- Experience with enterprise data platforms, data pipelines, SQL, and distributed data processing frameworks.
- Preferred qualifications include AI/ML experience in supply chain, healthcare, or other enterprise domains; MLOps and LLMOps experience; and familiarity with AI governance, model explainability, data security, privacy, and responsible AI.
Benefits
- Hybrid work arrangement in Bangalore.
- 10–20% travel required.
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About Solventum
At Solventum, we enable better, smarter, safer healthcare to improve lives. We never stop solving for you.