Machine Learning Engineer
IN, Bangalore
- Pay
- Pay not listed
- Work setting
Hybrid stated — work setting source
Understanding of API design and microservices architecture. Work location: Bangalore- Hybrid Travel: May include up to 10% domestic
Read the full posting- Employment
Full-time — employment source
Employment type in the employer’s listing: Full time
From the employer’s posting
What you’ll work on
Full postingBuild and maintain CI/CD pipelines for ML with automated testing and deployment
Deploy ML models as scalable APIs and microservices meeting latency needs
Develop and optimize ETL processes for FHIR and HL7 healthcare data
Implement monitoring for model performance, data drift and system health
Package and orchestrate ML workloads with Docker and Kubernetes
From the employer’s posting
Build and maintain CI/CD pipelines for machine learning, focusing on automated testing, model deployment, and version control
Deploy ML models as scalable APIs and microservices, ensuring they meet performance and latency requirements for clinical use.
Develop and optimize ETL processes to transform healthcare data (FHIR, HL7) into clean, usable datasets
Implement basic monitoring tools to track model performance, data drift, and system health in production.
Use Docker and Kubernetes to package and orchestrate ML workloads across different environments.
Education & alternatives
Your Skills and Expertise To set you up for success in this role from day one, Solventum requires (at a minimum) the following qualifications: - Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or a related field. - 3–5 years of professional experience in software engineering or data engineering, with at least 2 years focused on machine learning production environments.
Tools in this posting
- Java
- Python
- SQL
- AWS
- Azure
- dbt
- Docker
- GCP
- Kubernetes
- MLflow
- Prefect
- Spark
- Airflow
- pandas
- PyTorch
- scikit-learn
- Go
Source — Tool mentions in context
AND - Programming: Strong proficiency in Python and familiarity with SQL. Knowledge of a compiled language (like Go or Java) is a plus. - Cloud & Infrastructure: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) and containerization (Docker).
3. Engineering Best Practices - Code Quality: Write clean, maintainable, and well-documented Python code. Participate in code reviews to ensure system reliability. - Containerization: Use Docker and Kubernetes to package and orchestrate ML workloads across different environments.
- Programming: Strong proficiency in Python and familiarity with SQL. Knowledge of a compiled language (like Go or Java) is a plus. - Cloud & Infrastructure: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) and containerization (Docker). - ML Tools: Familiarity with ML libraries (PyTorch or Scikit-learn) and MLOps tools (like Airflow, Prefect, BentoML, or Kubeflow).
- ML Tools: Familiarity with ML libraries (PyTorch or Scikit-learn) and MLOps tools (like Airflow, Prefect, BentoML, or Kubeflow). - Data Tools: Experience with data processing frameworks (like Pandas, Spark, or dbt). Additional qualifications that could help you succeed even further in this role include:
- Code Quality: Write clean, maintainable, and well-documented Python code. Participate in code reviews to ensure system reliability. - Containerization: Use Docker and Kubernetes to package and orchestrate ML workloads across different environments. - Security & Compliance: Follow established protocols to ensure all data handling and deployments meet HIPAA and HITRUST security standards.
1. MLOps & Deployment - Pipeline Development: Build and maintain CI/CD pipelines for machine learning, focusing on automated testing, model deployment, and version control (using tools like MLflow or Git). - Model Serving: Deploy ML models as scalable APIs and microservices, ensuring they meet performance and latency requirements for clinical use.
- Cloud & Infrastructure: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) and containerization (Docker). - ML Tools: Familiarity with ML libraries (PyTorch or Scikit-learn) and MLOps tools (like Airflow, Prefect, BentoML, or Kubeflow). - Data Tools: Experience with data processing frameworks (like Pandas, Spark, or dbt).
Posting record
Report an errorOpen 5 monthsWe first saw this listing 175 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Apr 30, 2026 · 175 days ago
- Recorded sightings
- 317
- Last seen by us
- Oct 21, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
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Job description
Job Description:
At 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.
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 ML Engineer, you will be responsible for building and maintaining the pipelines that power AI in our Healthcare Information Systems (HIS). We are looking for a practical, detail-oriented engineer who is passionate about MLOps, data reliability, and production stability.
In this role, you won’t just be building models; you will be ensuring those models work reliably in the real world. You will help bridge the gap between data science and software engineering by implementing automated workflows, managing cloud infrastructure, and ensuring our AI services are secure and scalable.
Key Responsibilities
1. MLOps & Deployment
Pipeline Development: Build and maintain CI/CD pipelines for machine learning, focusing on automated testing, model deployment, and version control (using tools like MLflow or Git).
Model Serving: Deploy ML models as scalable APIs and microservices, ensuring they meet performance and latency requirements for clinical use.
Monitoring: Implement basic monitoring tools to track model performance, data drift, and system health in production.
2. Data Engineering & Integration
Data Pipelines: Develop and optimize ETL processes to transform healthcare data (FHIR, HL7) into clean, usable datasets for model training and inference.
Feature Management: Help build and maintain feature stores and data layers that ensure consistency between training and production environments.
System Integration: Work closely with backend teams to integrate ML outputs into our core healthcare applications.
3. Engineering Best Practices
Code Quality: Write clean, maintainable, and well-documented Python code. Participate in code reviews to ensure system reliability.
Containerization: Use Docker and Kubernetes to package and orchestrate ML workloads across different environments.
Security & Compliance: Follow established protocols to ensure all data handling and deployments meet HIPAA and HITRUST security standards.
Your Skills and Expertise
To set you up for success in this role from day one, Solventum requires (at a minimum) the following qualifications:
Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or a related field.
3–5 years of professional experience in software engineering or data engineering, with at least 2 years focused on machine learning production environments.
AND
Programming: Strong proficiency in Python and familiarity with SQL. Knowledge of a compiled language (like Go or Java) is a plus.
Cloud & Infrastructure: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) and containerization (Docker).
ML Tools: Familiarity with ML libraries (PyTorch or Scikit-learn) and MLOps tools (like Airflow, Prefect, BentoML, or Kubeflow).
Data Tools: Experience with data processing frameworks (like Pandas, Spark, or dbt).
Additional qualifications that could help you succeed even further in this role include:
Familiarity with deploying Large Language Models (LLMs) or using frameworks like LangChain.
Experience working in a regulated environment (Healthcare, Finance, etc.).
Understanding of API design and microservices architecture.
Work location: Bangalore- Hybrid
Travel: May include up to 10% domestic
Supporting Your Well-being
Solventum offers many programs to help you live your best life – both physically and financially. To ensure competitive pay and benefits, Solventum regularly benchmarks with other companies that are comparable in size and scope.
Solventum 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.
Please 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.
Please 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.
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
IN, Bangalore
- Understanding of API design and microservices architecture. Work location: Bangalore- Hybrid Travel: May include up to 10% domestic
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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