ML Engineer

Job description

ML Engineer — AI & Data Platform As an ML Engineer , you design, build, and operate production-grade machine-learning services on our AI & Data platform. You bridge robust software engineering with practical ML expertise: turning models, data products, and AI use cases into secure, scalable, observable services that deliver lasting business value. This role is intentionally software-engineering-led . We are looking for engineers who enjoy building reliable systems and have the ML knowledge to productionize them. You will work primarily with Databricks and AWS , partnering closely with data scientists, data engineers, cloud engineers, and product teams. What you will do Build, deploy, and operate ML-powered services, pipelines, and APIs using Python, SQL, Databricks, AWS, Linux, and infrastructure-as-code. Translate ML prototypes and business requirements into maintainable, production-ready software with clear interfaces, automated testing, and operational documentation. Own services throughout their lifecycle—from technical design and implementation through deployment, monitoring, incident resolution, and continuous improvement. Engineer reliable data and feature flows for training, evaluation, inference, and model retraining. Establish and evolve MLOps practices, including model versioning, experiment tracking, CI/CD, automated validation, release processes, observability, and rollback strategies. Build, maintain, and continuously improve reusable engineering standards, reference architectures, templates, and starter kits for ML and data projects—for example Databricks Asset Bundles (DABs) and AWS CDK templates. Turn platform standards into practical, well-documented building blocks that project teams can adopt with minimal friction. Advise, enable, and coach cross-functional project teams in designing efficient, scalable Big Data and ML solutions; explain architectural choices, guardrails, and delivery standards clearly and pragmatically. Review project implementations, identify gaps against platform standards, and support teams in resolving them without losing sight of delivery needs. Collaborate with platform, governance, infrastructure, data provisioning, scheduling, CI/CD, security, and data engineering teams to deliver integrated solutions. Improve platform standards, reusable components, developer experience, service reliability, and cost efficiency. Diagnose and resolve complex issues across applications, data pipelines, cloud services, and operating environments. Keep current with relevant developments in cloud engineering, MLOps, Databricks, and applied machine learning—and turn the useful ones into practical improvements. What you bring Software engineering 5+ years of professional software engineering experience , building and maintaining production systems. Strong proficiency in Python and experience with at least one additional programming language, such as Java, Scala, C++, or Go. A track record of designing clean, modular, maintainable, and well-tested software. Experience with code reviews, unit and integration testing, debugging, API design, dependency management, and secure software development practices. Strong knowledge of Git and modern collaborative development workflows. Experience working in agile, cross-functional product or platform teams. ML engineering and data platforms 2+ years of hands-on ML Engineering, MLOps, or closely related production ML experience . Experience taking ML workloads from experimentation into production, including training, evaluation, deployment, monitoring, and maintenance. Practical understanding of the ML lifecycle, common model-performance concerns, data quality, reproducibility, and model/version management. Strong SQL skills and experience working with relational databases, such as SAP HANA, Microsoft SQL Server, or MySQL. Experience with Big Data processing and/or a modern data platform; practical experience with Databricks is highly valued. Cloud and infrastructure 2+ years of hands-on AWS experience across several services, with the ability to make sound architecture and operational decisions. Experience using infrastructure-as-code, such as AWS CDK, Terraform, or CloudFormation . Strong Linux and operating system fundamentals, including networking, processes, permissions, logging, and troubleshooting. Experience with CI/CD pipelines and automated deployments. A pragmatic mindset for security, reliability, performance, observability, and cloud-cost management. Collaboration You approach ambiguous problems methodically, balance speed with quality, and take ownership of outcomes. You communicate technical decisions clearly to both engineering and non-engineering stakeholders. You enjoy collaborating, sharing knowledge, and raising engineering standards across teams. You have strong written and spoken English skills. Nice to have Databricks, AWS, Kubernetes, or Terraform certifications. Experience with Databricks capabilities such as Delta Lake, Workflows, MLflow, Unity Catalog, or model serving. Practical knowledge of AWS services such as Lambda, SageMaker, API Gateway, ECR, ECS/EKS, Step Functions, S3, IAM, CloudWatch, or SNS. Experience with containerization and orchestration, for example Docker and Kubernetes. Familiarity with Azure or Google Cloud Platform. Experience operating event-driven, distributed, or high-throughput data systems. Exposure to LLM applications, vector search, retrieval-augmented generation, or AI platform engineering. What success looks like Within your first months, you will have contributed to reliable, well-documented ML services that we can confidently run in production. You will help move ML use cases beyond prototypes by improving deployment quality, operational visibility, reuse, and cost efficiency—while making the platform easier for others to build on. Why this role matters Machine-learning initiatives create value only when they operate reliably in the real world. As an ML Engineer, you make that possible: you bring engineering discipline to ML workloads and help create a platform where data and AI teams can deliver secure, scalable, and sustainable products.