Lead AI Developer

Remote India

(View all jobs)

 

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

Hitachi Digital is a leader in digital transformation, leveraging advanced AI technologies to drive innovation and efficiency across our global operating companies (OpCos) and corporate functions. We are seeking an experienced Lead AI Developer to spearhead new AI initiatives and strengthen our Agentic AI capabilities. You will build new AI products and refine existing models to meet evolving business needs, with a primary focus on GCP and Gemini Enterprise.

 

The role

Design, develop, and implement production-grade AI models, agents, and algorithms using Agentic AI frameworks (multi‑agent orchestration, tool‑use, memory, judge/ranker patterns).
Lead delivery of Gemini Enterprise solutions (including Agent Workspace/Agent Builder or equivalent), defining agent roles, toolchains, and safety guardrails for enterprise workflows.
Drive new AI initiatives across OpCos and corporate functions, ensuring secure integration with existing systems, data sources, and processes.
Enhance and scale current Agentic AI capabilities for improved performance, latency, reliability, and cost efficiency.
Implement RAG (retrieval‑augmented generation) pipelines—document ingestion, embeddings, vector search, grounding, and citations.
Collaborate with product, engineering, data, and business teams to translate requirements into high‑impact AI features and APIs.
Establish LLMOps/MLOps practices (CI/CD, versioning, evaluation harnesses, rollback, prompt/test suites) and AI Ops observability (latency, accuracy, cost, drift/bias).
Troubleshoot complex production issues across models, agents, tooling, and integrations; drive root‑cause analysis and corrective actions.
Produce clear technical documentation—architecture decisions, model cards, playbooks, and best practices; mentor junior engineers and lead code/design reviews.
Stay current on the latest developments in LLMs/SLMs, embeddings, vector databases, safety/guardrails, and evaluation methodologies.

 

What you’ll bring

Requirements (Must‑Have)

Bachelor’s or master’s in computer science, Artificial Intelligence, Machine Learning, or related field.
7+ years in AI/ML development, including production delivery of LLM‑powered applications.
Hands‑on experience with Gemini Enterprise in real‑world deployments (prompting, tool‑use, safety, evaluation, cost/latency tuning).
Deep experience with Agentic AI frameworks (multi‑agent design, orchestration, tool/plugin ecosystems, agent memory, judge/critic patterns).
Primary experience on Google Cloud Platform (GCP), including Vertex AI (Pipelines, Endpoints/Model Serving, Model Registry/Monitoring, Vector Search) and secure service integration (IAM, Secret Manager, VPC‑SC).
Strong programming skills in Python (preferred) and one of Java/C++/TypeScript; proficiency building REST/gRPC services and event‑driven workflows.
Prior, demonstrable experience with Generative AI (GenAI) and Large Language Models (LLMs), including grounding/RAG and evaluation.
Practical expertise with AI/ML frameworks (e.g., TensorFlow, PyTorch) and NLP techniques.
Solid understanding of MLOps/LLMOps and AI Ops (observability, explainability, bias/drift monitoring, rollback strategies).
Excellent problem‑solving skills and the ability to work independently and within cross‑functional teams; strong written and verbal communication skills.

Preferred Qualifications

Experience integrating agents with enterprise systems (ERP, CRM, ITSM, Finance, Procurement) via APIs and eventing (Pub/Sub).
Background in vector databases, embeddings, and knowledge graphs (e.g., Neo4j) for contextual intelligence.
Exposure to GPU‑based training/inference, prompt optimization, and cost‑aware routing/ensembling of LLMs/SLMs.
Familiarity with orchestration libraries and evaluation tooling (e.g., LangChain/Graph, custom harnesses, judge/ranker patterns).
Certifications such as Google Professional Cloud Architect or Machine Learning Engineer.

What Success Looks Like (First 6–12 Months)

Launch of production Agentic AI features on GCP with measurable improvements in user satisfaction and business outcomes.
Established LLMOps/AI Ops dashboards and SLIs/SLOs (quality, latency, cost), with automated testing and safe rollback.
Documented guardrails and governance artifacts (model cards, policy checks, audit logs) meeting enterprise compliance standards.
Demonstrable reduction in latency/cost and uplift in answer quality via prompt tuning, retrieval optimization, and agent orchestration.

Core Competencies

Architectural thinking with a product mindset—balancing innovation, reliability, and compliance.
Operational excellence—instrumentation, telemetry, and continuous improvement.
Collaboration & leadership—clear communication across engineering, data, product, and business stakeholders.
Ownership & bias for action—ability to turn ambiguous problems into shipped AI capabilities.

Tools & Tech You’ll Use

Cloud & AI: GCP, Vertex AI (Pipelines, Endpoints, Model Registry/Monitoring, Vector Search), Gemini Enterprise.
Agentic AI: Multi‑agent orchestration, tool‑use frameworks, safety/guardrails, memory, judge/ranker patterns.
Retrieval: Embeddings, vector databases, RAG pipelines with grounding and citations.
Engineering: Python, Java/C++/TypeScript, REST/gRPC, Pub/Sub, CI/CD, containerized services.

 

About us