{"id":1812392,"url":"https://alion.io/job/solventum-corporation-senior-ai-ml-engineer","title":"Senior AI ML Engineer","company":{"id":6402,"name":"Solventum Corporation","domain":"solventum.com","url":"https://alion.io/company/solventum","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":79,"open_postings":64,"ghost_share":0,"stale_share":0.844,"repost_share":0,"time_to_fill_p50_days":36,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":48000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":41},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"CrewAI","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Grafana","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Prometheus","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-03T21:19:52Z","employer_posted_date":"2026-10-03","last_verified_at":"2026-10-31T17:44:45Z","board_verified":true,"closed_at":null,"days_open":28,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":28},"description":"At Solventum, we improve lives by helping healthcare professionals do their best work. Join a team using innovation and insight to make a real impact on the future of healthcare. All roles listed are with Solventum, and we’re committed to protecting your personal information-learn more in our Privacy Policy: https://www.solventum.com/en-us/home/legal/website-privacy-statement/applicant-privacy/Job Description:\nRole: Senior AI-ML Engineer / Data Scientist,\n3M Health Care is now Solventum\nAt Solventum, we enable better, smarter, safer healthcare to improve lives. As a new company with a long legacy of creating breakthrough solutions for our customers’ toughest challenges, we pioneer game-changing innovations at the intersection of health, material and data science that change patients' lives for the better while enabling healthcare professionals to perform at their best. Because people, and their wellbeing, are at the heart of every scientific advancement we pursue.\nWe 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.\nThe Impact You’ll Make in this Role\nAs an 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.\nIn 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.\nYou 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.\nKey Responsibilities\nMachine Learning Solution Development\nDesign, 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.\nDesign, 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.\nAgentic AI & Generative AI Engineering\nDesign, 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.\nDesign, 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.\nEnterprise Data Engineering & AI Integration\nDevelop 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.\nDevelop 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.\nCloud AI Deployment & MLOps\nDeploy 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.\nDeploy 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.\nModel Performance & AI Optimization\nEvaluate, 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.\nEvaluate, 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.\nTechnical Collaboration & Engineering Excellence\nPartner 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.\nPartner 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.\nYour Skills and Expertise\nBachelor’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.\nStrong 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.\nExperience 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.\nPractical experience with Large Language Models (LLMs), Prompt Engineering, RAG, AI evaluation techniques, and responsible AI practices.\nStrong understanding of system design patterns, microservices architecture, APIs, containerization (Docker), Kubernetes, and infrastructure automation.\nExperience 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.\nExperience working with enterprise data platforms, data pipelines, SQL, and distributed data processing frameworks to support AI/ML solutions.\n\nPreferred Qualifications:\nHands-on experience developing AI/ML solutions for Supply Chain, Healthcare, or other enterprise domains.\nExperience implementing MLOps and LLMOps practices, including model versioning, CI/CD pipelines, automated deployment, monitoring, observability, and AI lifecycle management.\nStrong understanding of system design patterns, microservices architecture, APIs, containerization (Docker), Kubernetes, and infrastructure automation.\nFamiliarity with AI governance, model explainability, data security, privacy, and Responsible AI practices.\n\nLocation - Bangalore (Hybrid)\n Travel - 10-20%\nSolventum is committed to maintaining the highest standards of integrity and professionalism in our recruitment process. Applicants must remain alert to fraudulent job postings and recruitment schemes that falsely claim to represent Solventum and seek to exploit job seekers.\nPlease note that all email communications from Solventum regarding job opportunities with the company will be from an email with a domain of @solventum.com. Be wary of unsolicited emails or messages regarding Solventum job opportunities from emails with other email domains.\nPlease note: your application may not be considered if you do not provide your education and work history, either by: 1) uploading a resume, or 2) entering the information into the application fields directly.\nSolventum Global Terms of Use and Privacy Statement\nCarefully read these Terms of Use before using this website. Your access to and use of this website and application for a job at Solventum are conditioned on your acceptance and compliance with these terms.\nPlease access the linked document by clicking here. 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