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Ml Ops Engineer

SRKay Consulting Group

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Location:
India , Bangalore

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

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Job Description:

We are hiring a ML Ops Engineer for our GCC client — Europe’s top retail brands. SRKay Consulting Group is a consulting firm that helps Fortune 500 companies set up and scale Global Capability Centers (GCCs) in India. This opportunity gives you exposure to enterprise-scale initiatives in retail and supply chain, working alongside a peer group of talented engineers, architects, and domain specialists across geographies in a collaborative, innovation-driven environment. It’s a role that not only sharpens your technical expertise but also provides long-term visibility and growth within a global organization.

Job Responsibility:

  • Pipeline Orchestration: Design, develop, and maintain complex ML workflows using Apache Airflow (Cloud Composer) to automate data ingestion, preprocessing, and model training
  • Lifecycle Management: Administer and scale MLflow for experiment tracking, model packaging, and maintaining a centralized Model Registry across the organization
  • Cloud & Hybrid Ops: Create and optimize training environments for custom ML/LLM models
  • Model Serving & Scaling: Architect high-performance inference endpoints and serve models via FastAPI/Flask with API Gateway
  • Infrastructure Management: Manage auto-scaling CUDA clusters on Google Kubernetes Engine (GKE)
  • CI/CD: Manage end-to-end delivery with Continuous Integration & Continuous Delivery (CI/CD)
  • Observability & Monitoring: Build dashboards to track model health, latency, and data drift

Requirements:

  • Workflow Management: Experience in managing Apache Airflow and Composer to support the Data Engineering components of grounded AI solutions
  • MLflow: Deep knowledge of MLflow Tracking, Projects, and Registry. Experience migrating MLflow backends between cloud providers
  • Workflow Tools: Familiarity with Vertex AI Pipelines and Azure DevOps for automation
  • GCP AI Services: Practical experience with Vertex AI (Workbench, Model Garden, Feature Store) and BigQuery ML
  • Containerization: Expert-level Docker and Kubernetes (GKE/AKS) skills. Must understand K8s operators and resource management for ML workloads
  • Infrastructure as Code (IaC): Proficiency in Terraform to manage reproducible cloud environments
  • Programming: Advanced Python skills with a focus on software engineering best practices (unit testing, modular design)
  • Data Engineering: Experience with Change Data Capture (CDC), Spark/PySpark, and optimizing data flow from BigQuery to training nodes
  • Access Control: Knowledge of IAM roles, VPC Service Controls, and securing ML endpoints
  • Experience with LLMOps (managing large-scale foundation models, prompt versioning, and vector database scaling)
  • Proven track record of building enterprise-scale ML infrastructure from scratch in Azure/GCP

Nice to have:

Related MLOps Engineering certifications are beneficial

Additional Information:

Job Posted:
May 03, 2026

Employment Type:
Fulltime
Work Type:
On-site work
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