About Bridgestone Mobility Solutions (BMS) :

We are Bridgestone Mobility Solutions, the digital product factory for the largest global tire company, and we are a team committed to product innovation in the transportation and mobility space.

With work spanning everything from tire integrated sensor and roadway vision recognition R&D to building mobile service and commercial vehicle marketplaces, we are defining the interconnected future of digital vehicles.

We are a team with a strong commitment to customer-driven innovation, data-based decision-making, and a commitment to learning through experimentation.

As a part of the Bridgestone team, the opportunities are endless across a broad spectrum of businesses in the Bridgestone portfolio.

Our culture of learning and growth has enabled our engineers to grow in leaps and bounds in the last decade. We are dedicated to having our engineering teams span the entire value chain from customer need to engineering to understand every aspect of the product team's job.

Job Brief :

Bridgestone GCC is building next-generation AI capabilities to power mobility solutions, customer support, fleet intelligence, retail optimization, and enterprise productivity.

We are looking for a hands-on GenAI Engineer / LLM Specialist who can design, build, and deploy production-grade AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and enterprise data platforms (Databricks).

This role will work closely with product, engineering and cloud teams to deliver secure, scalable, and business-impacting AI solutions.

Responsibilities :

GenAI & LLM Development :

- Design and implement LLM-powered applications (Voice AI agents, copilots, assistants etc).

- Build RAG pipelines using Databricks, vector databases, and enterprise knowledge sources.

- Optimize prompt engineering, embeddings, and evaluation frameworks.

- Implement guardrails, hallucination controls, and responsible AI practices.

Data & Platform Integration :

- Integrate AI solutions with SAP, Salesforce, telematics, APIs, and Java/Spring Boot services.

- Work with Databricks Lakehouse architecture for ingestion, feature engineering, and model lifecycle.

- Build scalable ML pipelines using MLflow.

Deployment & MLOps :

- Deploy models via Databricks, AWS, or Azure.

- Implement CI/CD for ML workflows.

- Monitor model performance, drift, and cost optimization.

- Ensure security, compliance, and governance standards.

Experience :

- 5+ years in AI/ML engineering.

- 1-3 years in GenAI / LLM development.

- Experience deploying AI to production environments.

Technical Requirements :

GenAI / LLM :

- Experience with OpenAI / Bedrock etc.

- LangChain / LlamaIndex / RAG frameworks.

- Prompt engineering & evaluation.

- Vector databases (Databricks Vector Search, Pinecone, etc.).

Programming :

- Python (strong).

- PySpark / SQL.

- REST APIs.

Data & Platform :

- Databricks (preferred).

- Delta Lake.

- MLflow.

- Data ingestion pipelines.

Cloud :

- AWS or Azure.

- Containerization (Docker).

- Basic Kubernetes understanding.

Works on problems of diverse scope where analysis of data requires evaluation of identifiable factor.

Partner with team members on functional and nonfunctional requirements and spread design philosophy, goals and improve the code quality across the team.