Join the team as our new ML Engineer, AI Center of Excellence (IC4)
India \u007C AI Center of Excellence
Protolabs is a digital manufacturing company that turns CAD files and technical drawings into physical parts - fast and at scale. Our AI Center of Excellence builds machine learning systems that empower core manufacturing workflows such as manufacturability analysis, predictive maintenance, and automated part routing to the right expert.
We are looking for a Machine Learning Engineer to join the AI COE and support ML and DL systems end-to-end - from problem framing and experimentation through to production deployment. You will work on complex, domain-specific ML problems in manufacturing, applying and adapting state-of-the-art approaches to new challenges and shipping solutions that run in live production environments.
Build & Own ML/DL Systems
- Apply ML/AI solutions with awareness of business needs, system constraints, and business context.
- Build and own ML/DL models across complex data types - geometries, part metadata, transactional data, and free-text notes.
- Own small-to-medium ML/DL subsystems and features end-to-end.
- Contribute NLP and document understanding pipelines for technical drawings and unstructured manufacturing specs; build reusable components on our AWS Bedrock-based AI Platform.
Experimentation & Technical Problem Solving
- Tackle different complex ML problems: identify data issues, navigate model choices, and design clear experiments.
- Work independently on assigned ML tasks while collaborating across teams.
- Suggest improvements at the feature level; explore and evaluate new techniques with guidance.
Collaboration & Mentorship
- Mentor junior peers to grow into ML; provide solid code, testing, and reviews.
- Contribute to feature-level design discussions and surface technical suggestions and improvements.
What It Takes:
Technical
- Good grounding in the mathematical foundations of ML and a relevant degree (computer science, simulation science, or equivalent).
- 3-5 years of hands-on experience designing, training, and deploying complex ML/DL models in production using state-of-the-art frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost).
- Proven ability to build models that serve as autonomous decision systems, applied to critical business problems with large, heterogeneous data.
- Strong Python engineering fundamentals: clean, modular, testable code; data pipelines; model versioning; CI/CD for ML; packaging for production (e.g. Docker, service wrappers).
- Solid ML experimentation practice: experiment tracking from scratch (e.g. W&B, MLflow), model evaluation, and iterative improvement tied to measurable business outcomes.
- Solid understanding of data transformation techniques, languages, and libraries (e.g. Pandas, Polars, SQL, dbt).
- Ability to work independently: break down larger problem definitions into concrete tasks, navigate model choices, and deliver production-ready ML implementations end-to-end.
Leadership & Collaboration
- Comfortable working independently on assigned ML tasks while staying closely coordinated with cross-functional teams.
- Willingness to mentor junior peers on code quality, testing, and ML best practices.
- Able to contribute meaningfully to feature-level design discussions and communicate technical trade-offs clearly.
Mindset
- Ownership-driven: takes small-to-medium ML/DL subsystems from problem framing through to production deployment.
- Curious and adaptable, applying and adjusting state-of-the-art approaches to new, domain-specific manufacturing problems.
- Outcome-focused, tying experimentation and iterative improvement to measurable business results.
Nice to Have
- Experience with manufacturing/industrial ML applications.
- Familiarity with LLM integration and Generative AI approaches.
- Experience with cloud-based ML infrastructure (e.g., AWS SageMaker, Snowflake).
- Exposure to MLOps practices: CI/CD pipelines, model monitoring, and observability.
- Experience collaborating in international teams across time zones.
What This Role Is Not:
- This is not a pure GenAI engineer role: you will develop complex DL models using tabular and non-tabular, domain-specific data - 3D geometries, CAD-derived features, technical drawings, machining data, and manufacturing metadata.
- This is not a Data Analyst role: models you build run in production and make final, customer-facing decisions, not small ML models that create recommendations for a human analyst.
