Role Overview
We are seeking a Principal Machine Learning Engineer to lead ML system architecture, enforce technical standards, and ensure controlled, audit-ready AI development. This role operates across ML design, regulatory alignment, and risk-aware SDLC, ensuring models are scalable, explainable, and defensible.
Key Responsibilities
ML Architecture & Technical Leadership
- Define reference ML architectures for training, inference, and monitoring
- Lead Next-Generation (NG) Models lifecycle (Quantification + Cycle Tracking + App)
- Drive optimal algorithm design and system scalability
- Enforce separation of deterministic logic vs ML components
- Standardize patterns for features, datasets, and evaluations
- Maintain the ML technical roadmap
- Review system and model designs across teams
QMS & Regulatory Alignment
- Translate product claims into ML requirements
- Maintain end-to-end traceability (Requirement → Model → Dataset → Validation → Release)
- Identify algorithmic risks and mitigations
- Contribute to SRS, RMF, and V&V artifacts
- Act as technical SME during audits
AI & Software Lifecycle (Controlled SDLC)
- Drive requirements-driven ML development
- Enforce versioning of code, models, and datasets
- Define verification vs validation triggers
- Establish release gates and rollback strategies
- Integrate post-market monitoring & model drift learnings
Requirements
- Strong experience in ML system design & architecture
- 8-12 years of experience required.
- Proven expertise in production-grade ML lifecycle
- Deep understanding of scalable and explainable ML systems
- Experience with controlled SDLC / risk-aware development
- Exposure to regulated / quality-driven environments preferred
What We’re Looking For
We’re looking for a system-level ML leader who can:
- Provide technical direction and architectural clarity
- Balance accuracy, latency, cost, and reliability
- Reduce algorithmic and regulatory risk
- Build traceable, defensible ML systems
- Clearly justify model behavior, decisions, and safety