Data Science Engineer

Cloudxtreme; Bengaluru, Mumbai; Hybrid; INR 2,500,000 - 3,200,000; Full time

Job summary

Cloudxtreme is seeking an MLOps Engineer to deploy, scale, and monitor ML and GenAI models in production. You will own the infrastructure, automation, and observability, collaborating with ML engineers and data scientists to ensure reliable serving at scale.

You will design and manage CI/CD pipelines, build scalable infra on Kubernetes and cloud services, automate workflows, and implement monitoring for latency, data drift, and failures. Leadership of a small team is involved.

Qualifications

• Design and manage CI/CD pipelines for ML deployment.
• Build scalable infra using Kubernetes, Docker, and cloud services.
• Automate model deployment and ensure reproducibility with versioning.
• Implement monitoring and observability for ML systems including data drift and latency.
• Integrate with Airflow/Prefect for retraining and inference workflows.

Responsibilities

• Manage model registry, versioning, and rollout strategies (blue/green, canary).
• Ensure security, compliance, and cost optimization for ML infra.
• Coordinate with ML engineers and data scientists to meet model requirements.
• Support real-time and batch inference with scalable infra.
• Document infrastructure-as-code practices (Terraform, Helm).

Skills

CI/CD pipelines

Kubernetes

Docker

Terraform

Monitoring/Observability

Education

B.E/B.Tech

Tools

Airflow

Prefect

Helm

OpenSearch

Milvus

Job description

Function/ Department: Data & Analytics.

Job Purpose : 'As an MLOps Engineer, you will be responsible for deploying, scaling, and monitoring ML & GenAI models in production. You will own the infrastructure, automation, and observability layer while collaborating with ML Engineers and Data Scientists to ensure models (LLMs, embeddings, recommendation systems, etc.) are reliably served at scale. This role combines DevOps best practices with ML-specific needs, ensuring smooth model lifecycle management and robust GenAI-powered applications.

Key / Primary Responsibilities:

• * Design and manage CI/CD pipelines for ML model deployment.
• * Provision and maintain scalable infrastructure using Kubernetes, Docker, AWS Lambda, or cloud services (AWS/GCP/Azure).
• * Automate model deployment workflows, ensuring reproducibility and version control.
• * Implement monitoring, alerting, and observability for ML systems (latency, throughput, data drift, failures).
• * Integrate with model orchestration pipelines (Airflow, Prefect) to support retraining and inference workflows.
• * Manage model registry, versioning, and rollout strategies (blue/green, canary deployments).
• * Ensure security, compliance, and cost optimization for ML infra.
• * Manage vector databases (Opensearch, Pinecone, FAISS, Milvus) for GenAI retrieval pipelines.

Secondary Responsibilities:

• * Work closely with ML Engineers and Data Scientists to understand model requirements.
• * Support real-time and batch inference pipelines by ensuring infra scalability and resilience.
• * Troubleshoot deployment and runtime issues across containers, APIs, and cloud services.
• * Document and standardize infrastructure-as-code practices (Terraform, Helm, etc.).
• * Manage GPU/accelerator infra for LLM fine-tuning or inference optimization.

Key Success Metrics:

Model Uptime and Reliability, Model Performance in production, Efficiency of Spark Jobs, Workflow Automation Efficiency, User Satisfaction and Internal Feedback.

Professional Degree:

Any B.E/B.Tech

Mandatory Certification:

Machine Learning or Data Science.

Managerial & Leadership Responsibilities:

Leading a team of 4 Data scientist and ML engineers to deliver on primary and secondary responsiblities..