Sr Machine Learning Engineer
Sr Machine Learning Engineer
India - Hyderabad Apply Now
JOB ID: R-236582
ADDITIONAL LOCATIONS:
India - Hyderabad
WORK LOCATION TYPE:
On Site
DATE POSTED: Feb. 11, 2026
CATEGORY: Engineering
Position Overview
The GCF5 Sr Machine Learning Engineer is the senior technical leader for the Agentic & ML Platform pillar. They define and socialize platform standards and patterns, lead multi-team delivery, mentor GCF4 engineers, and translate scientific needs into scalable ML/agentic platform designs. They own pillar-level adoption, reliability, and SLA/SLO outcomes, and influence cross-team engineering quality.
This role reports to the GCF7 leader and partners closely with peer GCF5 domain leads across SCIP to ensure cohesive, scalable platform evolution.
Core Responsibilities
- Own the ML and agentic platform technical roadmap within SCIP.
- Design and operationalize reusable ML/agentic infrastructure components enabling repeatable deployment.
- Define evaluation harnesses and model release gates.
- Establish monitoring, rollback, and observability practices for production ML systems.
- Implement guardrails and operational controls for safe agentic workflows.
- Define reproducibility standards and artifact versioning practices.
- Lead architecture reviews for ML platform evolution.
- Mentor engineers and elevate ML engineering rigor.
- Partner with research stakeholders to translate AI use cases into scalable platform capabilities.
Core Competencies
- Deep expertise in the assigned pillar (Agentic & ML Platform) (Agentic‑ML) with evidence of standard‑setting and reuse.
- Systems design at scale (ML); performance, security, and observability fundamentals.
- Product/engineering thinking: road mapping, prioritization, and outcome‑oriented delivery.
- Stakeholder influence across science, engineering, and governance forums; crisp written/verbal communication.
Core Success Measures
- Adoption rate of standardized ML platform components.
- Evaluation coverage across supported ML use cases.
- Reduction in model regressions and production ML incidents.
- Time-to-deploy new ML use cases.
- Reproducibility rate of experiments and deployments.
- Reduction in safe-use escalations.
Key Relationships
- Collaborates with GCF6 Group Lead and cross‑functional leaders (R&D/PD/Dev).
- Mentors and develops GCF4 Data and Software Engineers, partners with platform, data, ML, and research teams.
- Interfaces with governance (architecture, security, compliance) and vendor/partner teams.
Decision Authority
- Approve designs within the pillar; define and waive standards/patterns with rationale.
- Recommend buy‑vs‑build; commit pillar resources to meet SLAs/SLOs; escalate risks.
- Prioritize pillar backlog and roadmap in alignment with strategy and OKRs.
Qualifications
Basic Qualifications:
- BS+8 / MS+6 / PhD in CS/Engineering/Data disciplines.
- Demonstrated production delivery experience in ML/agentic platforms at scale.
- Demonstrated literacy in a relevant scientific domain (e.g., biology, chemistry, therapeutic discovery).
Preferred Qualifications:
- Depth in the assigned pillar (Agentic & ML Platform).
- Kubernetes and continuous integration/continuous delivery (CI/CD) at scale; observability, performance tuning, and security-by-design.
- Evidence of standard‑setting and cross‑team influence; mentoring experience.