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Principal Global Services
Senior Consultant - Machine Learning Engineering
Principal Global Services
Pune•On-site•3-5 Yrs•2 hours ago
Job description
Overview
As a Mid Level Machine Learning Engineer on our GenAI Enablement team, you'll work at the forefront of Large Language Model Operations (LLMOps), helping to design, develop, and scale our AI capabilities.
Responsibilities
- Enable the agility in data science delivery through automation across build, validation, deployment and monitoring of Data Science models. Monitor quality parameters for ML models in production. Shape and operate best practices for managing models in production. Contribute to solutions that accelerate the task of Production issue analysis by Data Scientists by enabling log viewing tracing and debugging of data science features in production.
Qualifications
- * Bachelor's Engineering degree - (preference in a computer science, technology, engineering or math-related field)
- 12+ years leading software/application development projects with strong delivery ownership
- Strong programming skills in AWS, python (Python programming skills from infrastructure development like lambda, App dev than data science)
- ML Engineering skills with software engineer/infra structure engineer with ML/AI background.
- Platform Ownership: Design and operate the ML platform (training, inference, orchestration, observability, governance).
- Delivery & Reliability: Establish SLAs/SLOs for model services; ensure uptime, scalability, and disaster recovery.
- MLOps Pipelines: Build/standardize CI/CD/CT (continuous training) for data + models + infra.
- Model Governance: Champion reproducibility, lineage, approvals, auditability, responsible AI, and compliance-by-default.
- Cost Management: Optimize cloud spend (compute/storage), autoscaling, and GPU allocation.
- Security & Risk: Secrets management, IAM, network policies, data privacy and model security.
- People Leadership: Hiring, coaching, technical direction, delivery rituals, stakeholder engagement.
- Change Management: Drive adoption, enablement, and documentation of best practices.
- Deployed & operated multiple production models (batch + real-time) with monitoring and rollback.
- Kubernetes, Terraform, registry + pipeline orchestration, and CI/CD for ML.
- Cloud-native stack (one major cloud) and infra security basics.
- Proven people leadership (hiring, mentoring, roadmap ownership).
- GenAI/LLMOps with RAG and LLM evals, Guardrails.