AI/MLOps RAG Specialist

Our Company

We’re Hitachi Digital, a company at the forefront of digital transformation and the fastest growing division of Hitachi Group. We’re crucial to the company’s strategy and ambition to become a premier global player in the massive and fast-moving digital transformation market. Our group companies, including GlobalLogic, Hitachi Digital Services, Hitachi Vantara and more, offer comprehensive services that span the entire digital lifecycle, from initial idea to full-scale operation and the infrastructure to run it on. Hitachi Digital represents One Hitachi, integrating domain knowledge and digital capabilities, and harnessing the power of the entire portfolio of services, technologies, and partnerships, to accelerate synergy creation and make real-world impact for our customers and society as a whole.

Imagine the sheer breadth of talent it takes to unleash a digital future. We don’t expect you to ‘fit’ every requirement – your life experience, character, perspective, and passion for achieving great things in the world are equally as important to us.

The Team

As an AI/MLOps RAG Specialist, you will own the end-to-end lifecycle of AI models and agents, focusing on deployment, monitoring, and optimization. You will leverage Google Cloud Platform (GCP) services and Vertex AI to build LLMOps pipelines, integrate RAG workflows, and enforce AI governance for transparency and reliability.

The Role

ML Ops & Pipeline Management

• Design and implement LLMxOps/MLOps pipelines for model training, deployment, and lifecycle management.
• Automate CI/CD workflows for AI models using GCP-native tools.
• Manage model versioning, rollback strategies, and performance benchmarking.

AI RAG & Agent Optimization

• Architect and deploy Retrieval-Augmented Generation (RAG) pipelines for contextual AI responses.
• Optimize AI agent performance (latency, accuracy, cost) through prompt tuning, caching, and orchestration strategies.
• Integrate knowledge graphs, embeddings, and vector databases for semantic retrieval.

Observability & Explainability

• Implement AI observability frameworks for monitoring agent workflows, latency, and reliability.
• Develop explainability dashboards for model decisions, bias detection, and compliance reporting.
• Define evaluation metrics and safety tests for AI outputs.

Cloud Architecture & Integration

• Leverage GCP services (Vertex AI, Cloud Run, GKE, IAM, VPC-SC) for secure and scalable deployments.
• Integrate AI solutions with enterprise systems and APIs.
• Ensure compliance with data governance and regulatory standards.

What You’ll Bring

• Bachelor’s or Master’s in Computer Science, AI/ML, or related field.
• 7+ years in ML Ops or AI platform engineering; 3+ years with GCP and Vertex AI.
• Hands-on experience with RAG architectures, LLMs, and Agentic AI workflows.
• Strong programming skills in Python and familiarity with Node.js for orchestration.
• Expertise in observability tools, model monitoring, and explainability frameworks.
• Knowledge of vector databases, embeddings, and semantic search.

Preferred Qualifications

• Certifications: Google Professional Cloud Architect, Machine Learning Engineer.
• Experience with LangChain, agent orchestration frameworks, and evaluation harnesses.
• Familiarity with data governance, bias detection, and AI safety protocols.
• Exposure to GPU optimization and cost-performance tuning.

Key Competencies

• Strong problem-solving and analytical skills.
• Ability to design scalable, secure, and explainable AI systems.
• Collaborative mindset with excellent communication skills.
• Passion for AI reliability, observability, and responsible AI practices.

Success Metrics

• Deployment of production-ready RAG pipelines integrated with enterprise workflows.
• Established AI observability dashboards and explainability reports.
• Reduction in model incident rates and improved response accuracy.
• Positive impact on AI governance and compliance readiness.

What You’ll Work With

• Cloud & AI: GCP, Vertex AI, Cloud Run, GKE.
• ML Ops Tools: CI/CD pipelines, monitoring frameworks, evaluation harnesses.
• Data & Retrieval: Vector databases, embeddings, semantic search.
• Agentic AI: Multi-agent orchestration, prompt optimization, RAG workflows.

Key skills/competency

• MLOps
• Retrieval-Augmented Generation (RAG)
• Google Cloud Platform (GCP)
• Vertex AI
• Large Language Models (LLMs)
• Python
• CI/CD
• AI Observability
• Vector Databases
• AI Governance