Senior ML Engineer
New
J
JobgetherFinancial services
This remote opportunity within IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- At least 5 years of experience building, deploying, and operating production software systems
- Required Skills
- DockerPythonSQLGitKubernetesCI/CDMLOpsDistributed Systems
Requirements
- At least 5 years of experience building, deploying, and operating production software systems.
- Strong programming skills in Python.
- Deep understanding of API design, distributed systems, service architecture, software design principles, testing strategies, and maintainable code.
- Experience designing and operating reliable, scalable cloud-native services or data-intensive systems in production.
- Practical experience with machine learning platforms, model serving, feature engineering, data platforms, MLOps, or related infrastructure.
- Familiarity with feature engineering, model training, evaluation, deployment, inference, monitoring, and governance.
- Experience with Docker, Kubernetes, CI/CD pipelines, Git-based workflows, and production observability tools.
- Practical experience with SQL, databases, caching systems, and tracing data or requests across distributed services.
- Ability to investigate complex technical problems, make architectural decisions, manage risks and dependencies, and deliver reliable solutions.
- Ability to lead engineering initiatives, mentor colleagues, and influence technical decisions across teams.
- Advantageous experience with AWS, Databricks, MLflow, Amazon SageMaker, Spark/PySpark, Kafka, or other streaming systems.
- Advantageous experience with feature platforms, model-serving infrastructure, workflow orchestration, or internal developer platforms.
Responsibilities
- Design, develop, deploy, and operate platform capabilities supporting the machine learning lifecycle.
- Build infrastructure for model deployment and serving, feature computation, data validation, workflow orchestration, monitoring, and production support.
- Develop reusable components, standards, and automation to improve reliability, scalability, observability, operational readiness, and cost efficiency.
- Translate business requirements into technical designs, evaluate options and trade-offs, coordinate delivery, and ensure production launches.
- Lead investigations into production incidents, identify root causes, and implement durable preventive solutions.
- Collaborate with Data Science, Product, Risk, Fraud, and Engineering stakeholders to deliver platform solutions.
- Contribute to platform architecture, establish engineering standards, and evaluate emerging technologies.
- Conduct code and design reviews and improve testing and development practices.
- Mentor engineers through knowledge sharing, pairing, and constructive feedback.
- Support the ML lifecycle across feature engineering, evaluation, deployment, serving, monitoring, and governance.
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