Senior Machine Learning Engineer

Location: Bangalore (Hybrid)
Experience: 6+ years
Employment type: Full-time
About the company
InCommon is hiring for our client a mission-driven US healthcare company that provides primary, behavioral, and social care to low-income patients with complex needs. Through at-home and virtual care, 24/7 support, and close partnerships with health plans, they walk alongside patients for their entire care journey.
Their care teams are powered by in-house technology and a commitment to evidence-based practice. If you want your engineering work to make a real difference in people's lives and help correct long-standing disparities in healthcare, this role is for you.
About the role
As a Senior Machine Learning Engineer, you will build and run the AI systems that help care teams deliver better, faster care. You will take ML and LLM models from idea to production, make sure they are reliable, fair, and well monitored, and build the tools that help the wider data team work faster. You will work closely with Product, Clinical, Data, and Design teams, and your decisions will have a direct impact on patients.
What you will do
Design, build, and maintain ML pipelines for training, evaluation, and deployment.
Develop and deploy LLM and transformer-based solutions in production.
Build scalable, secure ML infrastructure with strong monitoring and observability.
Create evaluation frameworks and benchmarks to measure model accuracy, reliability, and performance.
Champion responsible AI by checking models for fairness and equity across patient groups.
Set best practices for model versioning, experimentation, and reproducibility.
Assemble and work with large, complex healthcare datasets.
Build internal tools and libraries that help analysts and data scientists work more efficiently.
Partner with cross-functional teams and explain technical ideas clearly to non-technical stakeholders.
What we are looking for
5+ years of software engineering experience, with a focus on production ML systems, backend infrastructure, or MLOps.
Graduate degree in Computer Science, Statistics, or a related quantitative field.
Strong proficiency in Python and SQL, with clean, maintainable coding practices.
Hands-on experience building and maintaining ML pipelines, ideally with AWS SageMaker or Bedrock.
Proven experience developing LLM/transformer models and deploying ML models to production.
Experience with model versioning, experiment tracking, and reproducibility.
Experience improving ML infrastructure for stability, scalability, observability, and security.
Strong communication skills and comfort working with distributed, cross-functional teams.
Good to have
Experience in healthcare or health-tech, or with clinical, claims, or EHR data.
Familiarity with healthcare data privacy standards such as HIPAA.
Experience with tools like MLflow, Airflow, Docker, or Kubernetes.
Experience building LLM evaluation or responsible AI practices.
Prior experience working with US-based teams.

Skills
Airflow, Bedrock, ML, MLOps, MLflow, SQL, SageMaker, machine learning, python

Experience
6-10 yrs