eConsulting Sp. z o.o.
Senior Machine Learning Engineer (Python/MLOps)
- Compensation
- Not disclosed
- Location
- Not listed
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Contract
- Experience
- 5+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- At least 5 years of industry experience writing Python in a professional software or ML engineering context.
- Strong data engineering skills, SQL, and hands-on experience with workflow orchestration tools such as Airflow, Spark, or similar.
- A track record of deploying and operating ML models in production, including serving infrastructure, monitoring, and the realities of keeping models healthy over time.
- Solid software engineering fundamentals: clean, maintainable, well-tested code, and fluency in a shared codebase (feature branches, pull request reviews, collaborative development).
- Hands-on MLOps and CI/CD experience.
- Strong communication skills, including the ability to work with Data Scientists and other non-platform engineers without losing the technical plot, and to mentor others in good engineering practice.
- A degree in Computer Science or a related technical field, or equivalent practical experience.
Nice to have
- AWS experience (S3, EC2, ECS).
- Docker and containerised environments.
- Experience with Ray or other distributed compute frameworks.
- REST API integration at scale.
- Familiarity with geospatial data.
Full description
Workplace: hybrid, Warsaw
Workload: Full-time
Type of Contract: B2B
Join our fast-growing team as a Senior Machine Learning Engineer and shape the future of geospatial data analysis using a cutting-edge tech stack, including Ray Serve and AWS. In this role, you will take full engineering ownership of deploying advanced AI models, transforming raw data at a scale of hundreds of millions of rows into reliable, real-time services.
Key Responsibilities
- Owning the deployment of new risk models and scores into production on Ray Serve, and lifting the Insurance AI team's deployment maturity.
- Working with large-scale data sets (hundreds of millions of rows) and building custom workflows on top of existing platform foundations.
- Owning data transformation pipelines that turn semantic geospatial maps of a property into attributes suitable for categorical modelling.
- Collaborating with platform and infrastructure engineers to ensure solutions run reliably on our large data sets and accelerated model inferencing.
- Embedding MLOps and CI/CD best practice into the Insurance AI workflow, and feeding requirements back into the central platform.
Required Skills and Experience
- At least 5 years of industry experience writing Python in a professional software or ML engineering context.
- Strong data engineering skills, SQL, and hands-on experience with workflow orchestration tools such as Airflow, Spark, or similar.
- A track record of deploying and operating ML models in production, including serving infrastructure, monitoring, and the realities of keeping models healthy over time.
- Solid software engineering fundamentals: clean, maintainable, well-tested code, and fluency in a shared codebase (feature branches, pull request reviews, collaborative development).
- Hands-on MLOps and CI/CD experience.
- Strong communication skills, including the ability to work with Data Scientists and other non-platform engineers without losing the technical plot, and to mentor others in good engineering practice.
- A degree in Computer Science or a related technical field, or equivalent practical experience.
Nice to have
- AWS experience (S3, EC2, ECS).
- Docker and containerised environments.
- Experience with Ray or other distributed compute frameworks.