1 day ago
Bengaluru, IndiaSenior
Responsibilities
- Design, develop, deploy, and optimize scalable machine learning models and AI solutions for supply chain challenges.
- Build end-to-end ML pipelines for forecasting, optimization, predictive analytics, anomaly detection, and intelligent automation.
- Develop, test, and deploy AI agents and multi-agent systems using LLMs, tool integration, memory, and orchestration.
- Create data ingestion, transformation, feature-engineering, and enterprise AI integration pipelines.
- Deploy and manage AI and ML applications on AWS and Azure while maintaining MLOps and LLMOps pipelines.
- Evaluate and monitor machine learning models and LLMs, improving accuracy, latency, cloud-resource utilization, reliability, and cost.
- Implement responsible AI practices and AI observability.
- Collaborate with business and technical stakeholders, contribute to architecture and code reviews, establish engineering standards, and create technical documentation.
Requirements
- Bachelor’s degree in computer science, software engineering, AI, or a related field with 7+ years of professional experience in machine learning, artificial intelligence, data science, or AI engineering, or a master’s degree with 5+ years of relevant experience.
- Strong hands-on Python expertise and experience designing, deploying, and optimizing production machine learning and AI solutions.
- Experience with predictive modeling, forecasting, optimization, feature engineering, model evaluation, monitoring, and lifecycle management on cloud platforms such as Azure, Databricks, and AWS.
- Experience building and deploying production-grade generative AI applications and agentic AI workflows using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or similar technologies.
- Practical experience with LLMs, prompt engineering, RAG, 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 including 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 experience developing AI/ML solutions for supply chain, healthcare, or other enterprise domains.
- Preferred experience implementing MLOps and LLMOps practices, AI governance, model explainability, data security, privacy, and responsible AI.
Benefits
- Hybrid work arrangement in Bangalore.
- 10–20% travel.
- Solventum recruitment communications use the @solventum.com email domain.