Research intern applying machine learning techniques to impactful projects in Seagate's Software and Applied AI Research team. Opportunity to work on AI solutions, predictive maintenance, and more.
About the role
- Senior Machine Learning Engineer at Mastercam focusing on developing production-ready AI integrations in CAM applications. Leading AI solution architectures for efficient manufacturing processes.
Responsibilities
- Design, develop, and optimize machine learning and deep learning models using Python-based frameworks such as scikit-learn, TensorFlow, PyTorch, etc.
- Apply ML/DL techniques to solve real-world problems in CAD/CAM and manufacturing workflows.
- Perform feature engineering, model evaluation, validation, and performance optimization.
- Translate research ideas and prototypes into robust, production-ready AI solutions.
- Design and implement agentic AI workflows using commercial and open-source AI toolkits.
- Build proof-of-value (PoV) solutions rapidly using: LLMs, Multi-agent systems, Tool-using and orchestrated agents.
- Build end-to-end AI solutions, from ideation and experimentation to deployment and long-term maintenance.
- Integrate ML/AI capabilities into production-grade C++ applications that form part of the Mastercam product.
- Collaborate closely with product, platform, and core engineering teams to ensure performance, scalability, and reliability.
- Implement robust observability for AI features (telemetry, tracing, quality signals, latency/cost metrics) to support continuous improvement and operational reliability.
- Establish and follow engineering best practices (code quality, automated testing, CI/CD, secure development practices) for AI-enabled services and integrations.
Requirements
- Bachelors in Mechanical/Industrial Engineering with Masters in AI/ML from reputed institutes like IIT, BITS, NIT or equivalent global institutions.
- 10+ years of professional experience delivering software and/or machine learning solutions to production.
- Strong applied ML background with proven ability to deliver product outcomes (not research-only), including: Problem framing, data requirements, experimentation, and iteration.
- Model evaluation, deployment, monitoring, and operational ownership.
- Hands-on experience integrating LLMs into products, including at least several of: Prompting and structured outputs, Tool/function calling, Retrieval/RAG and embeddings, Evaluation methods to measure quality and prevent regressions.
- Ability to choose the right approach per use case (and explain tradeoffs): prompting vs RAG vs fine-tuning vs small dedicated models vs rule/tool-based automation.
- Practical experience building lightweight ML models for limited-scope decision-making (e.g., classifiers/rankers) under constraints (latency, cost, maintainability).
- Strong hands-on experience with cloud ML platforms, preferably Microsoft Azure (current primary provider), including some of: Training pipelines, model packaging, deployment, monitoring, cost/latency management.
- Strong engineering fundamentals: API/service integration, code quality, testing mindset, and collaborative development practices.
- Practical experience building reliable and safe automation, including guardrails such as: Permissions/scoping, auditability, traceability, rate limiting, retry/recovery, and human-in-the-loop patterns.
- Experience with MCP (Model Context Protocol) and/or designing tool ecosystems for agentic workflows.
Benefits
- Competitive salary
- Health insurance
- Flexible work arrangements
- Professional development opportunities
Job title
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