MLOps Engineer – GCP | ML, AI & GenAI | Immediate Joiner

Job Description

Job Title: MLOps Engineer (GCP) – ML / AI / Gen-AI

Job Type :: Chennai,

Experience: 5+ Years

Role Level: Engineer II (Practitioner)

Employment Type: Full-Time

Position Overview

Ford Customer Service Analytics is seeking a skilled MLOps Engineer (GCP) to productionize, deploy, and operate machine learning solutions on Google Cloud Platform (GCP). This role focuses on building scalable ML deployment pipelines, ensuring reliability and observability of ML services, and establishing standardized MLOps best practices across teams.

The engineer will also support ML, AI, and Generative AI (Gen-AI) development activities such as experimentation, integration, evaluation, and prompt-based workflows as needed.

Key Responsibilities

MLOps & Production Engineering

• Productionize machine learning models on GCP, supporting both batch scoring and real-time inference use cases.
• Build and maintain automated ML CI/CD pipelines for testing, packaging, versioning, promotion, rollback, and release management.
• Implement end-to-end ML workflows, including:
• Data preparation
• Model training and validation
• Deployment
• Monitoring and lifecycle management
• Deploy and maintain inference APIs and services, ensuring high performance, scalability, reliability, and uptime.

Monitoring, Reliability & Operations

• Implement observability for ML systems, including:
• Logging, metrics, and alerts
• Model performance monitoring
• Data quality checks
• Concept and data drift detection
• Manage and optimize cloud infrastructure fundamentals, including:
• IAM and access controls
• Secrets management
• Networking and security
• Reliability engineering
• Cost and performance optimization

Data & Platform Collaboration

• Collaborate with Data Scientists, ML Engineers, and Analytics teams to operationalize datasets and features using BigQuery and Cloud Storage (GCS).
• Support orchestration workflows as required (Airflow / Cloud Composer).
• Promote standardized MLOps patterns, tooling, and best practices across teams.

AI / Gen-AI Enablement

• Support ML, AI, and Gen-AI development tasks when required, including:
• Prompt workflows
• Retrieval-Augmented Generation (RAG) patterns
• Model evaluation and experimentation
• Integration of Gen-AI capabilities into production systems

Required Skills & Qualifications

• Bachelor’s degree in Computer Science or related discipline (Master’s preferred).
• 3+ years of experience in Software Engineering, ML Engineering, MLOps, or Production ML.
• Strong hands-on experience with GCP for production ML, including:
• Cloud Run (preferred)
• GKE and/or Vertex AI (acceptable alternatives)
• Proficiency in Python with strong software engineering fundamentals:
• API development
• Testing frameworks
• Git/version control
• Code quality and best practices
• Hands-on experience with Docker and CI/CD pipelines (Cloud Build, GitHub Actions, Jenkins).
• Experience with Infrastructure as Code (Terraform preferred).
• Experience using BigQuery and Google Cloud Storage.
• Proven experience implementing monitoring and operational practices for ML systems, including reliability, performance, and drift detection.

Desired / Preferred Skills

• Practical Generative AI experience, including:
• Gemini / Vertex AI
• Embeddings
• RAG architectures
• Prompt engineering
• Familiarity with orchestration tools such as Apache Airflow / Cloud Composer.
• Exposure to AI/ML evaluation frameworks and production AI integration patterns.

Core Skills Summary

Required:

• GCP
• MLOps
• Machine Learning Deployment
• DevOps
• API Development
• Docker
• CI/CD

Preferred:

• AI / ML
• Generative AI

Experience Level

• Engineer II (Practitioner)
• 3+ years of relevant hands-on experience in production ML environments

Education

• Required: Bachelor’s Degree
• Preferred: Master’s Degree (Optional)