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Sr Machine Learning Engineer

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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.
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