MLOps Engineer
MLOps engineer who productionizes machine learning, GenAI, and decision-science solutions on Google Cloud Platform (BigQuery, Dataproc, Dataflow, Vertex AI, Gemini) — building automated data/ML pipelines, services/APIs, CI/CD, and monitoring alongside data scientists. Needs 6+ years' experience with strong Python and SQL.
- Compensation
- ₹3M–₹4M / yr
- Location
- Bangalore
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 6+ years
- Category
- Devops
- Posted
Skills
Must have
- 6+ years of relevant experience in MLOps, software engineering, cloud engineering, machine learning engineering, or a related technical field
- Strong software engineering skills for maintainable, production-quality applications, services, or technical solutions
- Strong Python skills and experience in Python-based data science or ML environments
- Experience building and operating cloud-based data and ML pipelines (data processing, orchestration, automation, production integration)
- Experience deploying and operating machine learning models or analytical solutions in production
- Strong SQL skills and experience with large analytical datasets and cloud-based data environments
- Experience with modern software engineering practices: source control, testing, code review, CI/CD, dependency management, multiple deployment environments
- Understanding of the machine learning lifecycle and experience working closely with data scientists
- Experience developing or integrating APIs, services, or applications to operationalize data and AI/ML solutions
- Strong troubleshooting and problem-solving skills across software, data, and cloud environments
- Ability to learn new technologies quickly and apply them to evolving requirements
Nice to have
- Experience with Google Cloud Platform and cloud-native AI/ML architectures
- Experience operationalizing generative AI, LLM, or agentic solutions
- Familiarity with model registries, ML pipelines, model monitoring, experiment tracking, and model lifecycle management
- Experience with infrastructure automation and modern cloud deployment approaches, including managed cloud services and/or containerized applications
- Experience designing reusable technical solutions supporting multiple models, use cases, or clients
- Healthcare experience with medical claims, pharmacy claims, eligibility, clinical, or member data
Full description
Role : MLOps EngineerPosition Summary :
We are seeking an MLOps Engineer to help build and operate the engineering foundation that moves our machine learning, generative AI, and decision science solutions from development into production.
Working closely with data scientists and other technical teams, this role is responsible for transforming models and analytical solutions into reliable, scalable, production-ready capabilities. The role combines MLOps, cloud engineering, software development, data and ML pipelines, and solution implementation.
Because we operate as a small, highly collaborative team, this is a broad hands-on engineering role. The MLOps Engineer may build a production ML pipeline, develop a service or application component, optimize an existing process, contribute to solution architecture, troubleshoot a production workflow, or help operationalize a new GenAI capability depending on the needs of the initiative.
Key Responsibilities :
- Partner closely with data scientists throughout the development lifecycle to move predictive models, analytical solutions, and GenAI capabilities from experimentation into production.
- Build and maintain automated data and ML pipelines supporting data preparation, model execution, scoring, deployment, and downstream operational use.
- Develop and maintain software, services, APIs, utilities, and application components required to operationalize AI/ML solutions.
- Contribute to the design and evolution of cloud and ML architecture, helping identify scalable and reusable approaches across models, clients, and use cases.
- Build and improve deployment automation, CI/CD, versioning, testing, environment management, reproducibility, and production release processes.
- Implement monitoring and observability across ML pipelines and production solutions, including failures, performance, reliability, data quality, and resource utilization.
- Optimize application code, queries, data processing, pipelines, and cloud resources for performance, scalability, maintainability, and cost.
- Support the productionization of generative AI and agentic capabilities, including integration with enterprise data, APIs, applications, and operational workflows.
- Design and maintain the data structures and processing workflows required to support model development, scoring, monitoring, and production applications.
- Troubleshoot issues across applications, cloud services, data pipelines, and ML workflows and collaborate with data scientists and engineers to identify durable solutions.
- Help establish reusable engineering patterns and standards for moving AI/ML capabilities through development, testing, production, monitoring, and ongoing maintenance.