Join a high-impact engineering team to deploy and monitor machine learning models at scale. You will build robust APIs, optimize data pipelines, and ensure model performance in production environments. This role emphasizes collaboration, reliability, and scalable system design.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
Join a high-impact engineering team bringing machine learning models into production at scale.
We’re looking for an ML Operations Bridge Engineer who thrives at the intersection of data science and software engineering. You’ll take ML models from notebooks to production APIs serving millions of predictions, build robust data pipelines, and ensure model performance in real-world environments.
What you’ll do
- Deploy and monitor machine learning models in production environments.
- Build and maintain backend APIs and services (TypeScript/Python).
- Develop and optimize real-time data pipelines (ETL/ELT, streaming, validation).
- Collaborate closely with data scientists and engineers to integrate ML features.
- Work with cloud platforms (GCP, AWS, or Azure) and CI/CD workflows.
- Write optimized SQL queries for large-scale datasets.
- Design scalable systems that ensure reliability, observability, and low latency.
What we’re looking for
- 3+ years of professional experience with Python, taking models to production.
- 2+ years building backend services or APIs (TypeScript/JavaScript or similar).
- Proven experience in MLOps — deployment, pipelines, monitoring.
- Strong SQL skills and understanding of data engineering workflows.
- Hands-on experience with cloud infrastructure (GCP, AWS, or Azure).
- Experience with CI/CD systems for ML or backend applications.
- Strong collaboration, analytical, and problem-solving abilities.
Nice to have
- Experience with Kafka or other streaming platforms.
- Familiarity with serverless ML runtimes or managed ML platforms.
- Exposure to Elixir or functional programming.
- Knowledge of logistics, e-commerce, or supply chain domains.
- Experience with feature stores, edge computing, or Kubernetes.
- Contributions to open-source ML or data engineering projects.
Who you are
- A clear communicator and proactive collaborator.
- Detail-oriented with strong troubleshooting skills.
- Excited to bridge the gap between machine learning and production systems.
- Production-minded, you prioritize reliability, scalability, and measurable impact.
Why join us?
✅ Work on cutting-edge ML infrastructure that powers real business impact.
✅ Collaborate with world-class engineers and data scientists.
✅ Shape how machine learning models are deployed and scaled in production.
✅ 100% remote, flexible, and impact-driven environment.
👉 If you’re passionate about taking ML from research to production and thrive in cross-functional teams, apply now to join our team as an ML Operations Bridge Engineer.