MLOps / LLMOps Engineer

Remote

Fulltime Permanent Role

Exp. - 5+ Years

Max Salary Range 25 LPA

Job Summary

As an MLOps / LLMOps Engineer, you will design, automate, and operate scalable ML and

LLM systems on our client enterprise Lakehouse platform.

You will work closely with Data Science, Engineering, and Product teams to deploy reliable,

secure, and production-ready ML and GenAI solutions. This role focuses on operationalizing

ML models, building CI/CD pipelines, ensuring governance and compliance, and maintaining

high-performance, observable AI systems.

Primary Responsibility

ML/LLM Platform Development

• Operationalize model training, evaluation, packaging, and deployment using

Databricks, Delta Lake, and medallion architecture.

• Implement Unity Catalog model governance, lineage tracking, and access control.

• Develop reusable job templates, cluster policies, and standardized deployment

patterns.

ML/LLM Production Deployment

• Deploy and manage ML and GenAI solutions including risk scoring, anomaly detection,

predictive maintenance, NLP, and RAG pipelines.

• Build and optimize LLM pipelines using vector databases, model serving endpoints,

and inference workflows.

• Optimize models using quantization, caching, and performance tuning techniques.

• Implement batch and real-time inference pipelines with defined SLAs.

Reliability, Security & Compliance

• Implement data contracts, schema validation, and data quality checks across ML

pipelines.

• Ensure secure handling of sensitive data including PII detection, classification, and

obfuscation.

• Maintain full lineage from data sources to deployed models and serving endpoints.

• Enforce data residency, governance, and compliance policies.

CI/CD Automation & Testing

• Implement CI/CD pipelines using GitHub Actions and Databricks Asset Bundles.

• Automate deployments across DEV, QA, and PROD environments.

• Develop unit and integration tests for data pipelines and ML models.

• Ensure version control, reproducibility, and automated deployment workflows.

Observability & Operations

• Monitor pipeline health, model performance, drift, and system reliability.

• Implement alerting, incident response workflows, and automated ticketing.

• Track LLM performance metrics including latency, hallucination rates, and API costs.

• Develop runbooks, disaster recovery procedures, and operational documentation.

FinOps & Cost Optimization

• Apply tagging policies and cost tracking for ML infrastructure.

• Support budget monitoring, cost optimization, and resource management.

Skills & Experience

Required:

• 3–5 years of experience in MLOps, LLMOps, or ML platform engineering roles.

• Hands-on experience with Databricks, Delta Lake, Unity Catalog, and ML deployment

workflows.

• Strong experience with CI/CD pipelines using GitHub Actions and infrastructure

automation.

• Experience implementing data quality validation, schema governance, and data

contracts.

• Experience building production-grade ML pipelines with monitoring and observability.

• Strong security knowledge including RBAC, encryption, data residency, and

governance practices.

• Proficiency in Python, SQL, and distributed data processing frameworks.

Preferred:

• Experience with LLM pipelines, prompt engineering, RAG workflows, and model

optimization.

• Experience with vector databases, model serving, and MLflow.

• Experience with Azure and AWS cloud platforms, including security and networking.

• Experience with geospatial data and analytics.

• Familiarity with Power BI, semantic layers, and enterprise analytics platforms.

• Experience with disaster recovery, FinOps, and enterprise-scale ML operations.

EDUCATION

• Bachelor's or master's degree in computer science, Software Engineering, or a related

field, or equivalent professional experience.

MLOps / LLMOps Engineer (Mid-Level)