Lead - ML Ops Engineer

What will you do?

• Design, develop, and document Infrastructure as Code (Terraform) for ML/LLM platform components on AWS/Databricks; implement secure, scalable foundations for data, compute, networking, and secrets.
• Build and maintain GitHub based pipelines (Actions/Workflows) for training, packaging, validation, and deployment of ML/LLM assets (models, evaluation suites, prompts, policies), using GitOps for environment promotion.
• Containerize models using Docker and deploy them primarily through managed endpoints (SageMaker/Azure ML); Kubernetes ‑ based serving (KServe/Triton/Seldon) is a plus.
• Operate model registries and feature stores; enforce versioning, lineage, and artifact governance via MLflow/Databricks and cloud native services.
• Implement logs/metrics/traces, performance profiling, and drift/quality monitors; define SLIs/SLOs and on call runbooks; drive incident response and post-mortems with accountability (business hours support rotation).
• Embed DevSecOps: secrets management, IAM/RBAC, vulnerability scanning, image signing, policy as code, least privilege access, backup/DR/resiliency patterns; align with enterprise security standards.
• Operationalize GenAI: prompt/content safety filters, evaluation harnesses (human in the loop), grounding/attribution logging, token cost & latency tracking, and red teaming pipelines integrated into CI/CD.
• Monitor and optimize compute/storage/bandwidth and inference costs; implement right sizing, autoscaling, and caching strategies.
• Partner with Data Scientists to productize models; co design platform features with stakeholders; deliver documentation, templates, and knowledge transfers that accelerate safe reuse.
• Run operations (RUN): Troubleshoot escalations, improve monitoring, automate administration/IRP tasks, and continuously harden reliability, performance, and security across environments.

• • Technical Experience:
  • Understanding of DevOps concepts such as reference implementation enforcement, use of shared DevOps stacks, infrastructure optimization (performance, cost, HA, resiliency), release management (GitOps best practices), and QA automation frameworks.
  • Strong knowledge of AWS ecosystems and Databricks integration.
  • Proficiency in Terraform for developing, testing, and maintaining Infrastructure ‑ as ‑ Code to manage cloud services for ML engineering.
  • Hands ‑ on experience with CI/CD using GitHub, GitHub Actions, and Workflow automation to support continuous integration, delivery, and deployment of ML assets.
  • Strong experience with Docker; Kubernetes is a plus.
  • MLflow (tracking/registry), model registries, feature stores, experiment tracking, and lineage management; Databricks and cloud native equivalents.
  • Build pipelines for training, testing (unit/integration/e2e), evaluation, and deployment.
  • Experience designing or contributing to infrastructure, application, and performance monitoring (logs, metrics, dashboards) and supporting observability strategies.
  • Ability to produce efficient, maintainable code in Python; experience troubleshooting and extending Python ‑ based services.
  
  
  
  • Consulting Experience:
  • Proven track record in an IT consulting environment, engaging with large enterprises and MNCs in strategic data solutioning projects.
  • Experience working with enterprise stakeholders in platform adoption, requirement clarification, effort sizing, and change management for ML platform rollouts.
  
  
  
  • Leadership & Soft Skills:
  • Strong collaboration and communication across Delivery and RUN.
  • Excellent communication, documentation, and presentation skills.
  • Strong problem-solving, analytical thinking, and strategic vision.