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Salary
$31k – $79k per year (Estimated)
Location
In office (Coimbatore)
Seniority
Middle · 10+ years exp
Employment
Full-Time
Overview
Company
Impact
Profile match
Bosch is a German engineering and technology group founded in Stuttgart in 1886 by Robert Bosch as a workshop for precision mechanics and electrical engineering. It is the largest automotive supplier in the world, making braking, steering, powertrain, sensor and driver assistance systems for nearly every carmaker, and it also produces power tools, home appliances through BSH, industrial drives through Rexroth, and building security and heating systems. Around ninety per cent of the company is owned by a charitable foundation, an ownership structure that gives it unusual freedom to invest across long research cycles including semiconductors and hydrogen.

Bosch Global Software Technologies Private Limited is a 100% owned subsidiary of Robert Bosch GmbH, one of the world's leading global supplier of technology and services, offering end-to-end Engineering, IT and Business Solutions. With over 27,000+ associates, it’s the largest software development center of Bosch, outside Germany, indicating that it is the Technology Powerhouse of Bosch in India with a global footprint and presence in the US, Europe and the Asia Pacific region.

Roles & Responsibilities :

We are seeking an experienced AI Expert who can independently design, develop, and deploy Generative AI solutions and embed AI features into Bosch products. The ideal candidate has hands-on experience building LLM/SLM-based applications and agentic systems on Azure and/or AWS, from proof-of-concept through production deployment.

This is an end-to-end engineering role. The candidate is expected to own the full lifecycle of Gen AI solutions - problem framing, architecture, model selection, prompt and retrieval design, agentic workflow development, evaluation, deployment, and monitoring - while working closely with product teams to translate business needs into reliable, scalable AI capabilities. Experience applying agentic approaches within the software development lifecycle (Agentic SDLC) is strongly valued.

Key Responsibilities

  • Generative AI Solution Development (LLM / SLM):

- Design, develop, and deploy production-grade Gen AI solutions using Large Language Models (LLMs) and Small Language Models (SLMs).

- Build and optimize Retrieval-Augmented Generation (RAG) pipelines, including chunking, embeddings, and vector database integration (e.g., Azure AI Search, ChromaDB, FAISS, Pinecone).

- Apply advanced prompt engineering, few-shot / structured prompting, function / tool calling, and output validation techniques.

- Select, fine-tune, and adapt models appropriately, balancing accuracy, latency, and cost across LLM and SLM options.

  • Agentic AI Development:

- Design and build multi-agent and single-agent systems using agentic frameworks such as LangGraph, LangChain, AutoGen, or CrewAI.

- Implement agent orchestration, tool / function integration, memory, state management, and Model Context Protocol (MCP) based tooling.

- Apply agentic approaches to the software development lifecycle (Agentic SDLC) requirements, code generation, review, testing, and documentation automation.

- Build Knowledge Graphs for effective content retrieval.

- Perform log data analysis and identify anomalies based on log and system-failure data.

- Optimize agents and model token cost using LiteLLM Proxy and similar tools.

- Apply domain-based fine-tuning and Small Language Models (SLMs) for code generation.

- Onboard AI services in the API Marketplace and Gen AI Marketplace.

- Understand and apply Bosch compliance and processes for AI solution development and deployment.

  • Cloud Engineering & Deployment (Azure / AWS):

- Develop and deploy Gen AI solutions on Azure (Azure OpenAI, Azure AI Foundry / AI Studio, Azure AI Search) and/or AWS (Bedrock, SageMaker).

- Containerize and deploy services using Docker; implement CI/CD pipelines for reliable, repeatable releases.

- Apply MLOps / LLMOps practices - versioning, evaluation, observability, cost monitoring, and guardrails - for production stability.

  • Bosch Product Integration:

- Build and embed Gen AI features into Bosch products and internal platforms, working independently as an AI expert developer.

- Translate product and business requirements into AI capabilities and prototype rapidly to validate feasibility.

- Ensure solutions meet enterprise standards for security, data privacy, responsible AI, and compliance.

  • Collaboration, Documentation & Communication:

- Work closely with cross-functional teams - product, software, QA, and architecture - to integrate AI into the product lifecycle.

- Maintain clear architecture, design, and operational documentation.

- Communicate effectively with stakeholders and present solution options and trade-offs.

Educational qualification:

BE/BTech/MCA/MSc/MTech in CS, IT, AI/ML or related field

Experience :

  • 10 to 15 years of overall software / IT experience, with 5 to 7 years of hands-on Generative AI experience.

Mandatory/requires Skills :

  • Strong hands-on experience developing and deploying Gen AI solutions using LLMs and/or SLMs.
  • Proven experience with at least one major cloud platform - Azure or AWS - for AI workloads.
  • Hands-on experience with at least one agentic AI framework (LangGraph, LangChain, AutoGen, or CrewAI).
  • Strong Python programming skills and experience with REST APIs, Git, and Docker.
  • Solid understanding of RAG, Knowledge Graphs, LLM routing, prompt engineering, embeddings, and vector databases.
  • Excellent problem-solving skills and the ability to work independently end-to-end.
  • Strong communication and stakeholder-management skills.

Preferred Skills :

  • Experience building Gen AI solutions for Bosch software or mechanical products, or in an enterprise product environment.
  • Experience in full software development and deployment (end-to-end SDLC ownership).
  • Hands-on experience with Agentic SDLC tooling (e.g., GitHub Copilot, Claude Code, spec-driven / agentic development workflows).
  • Experience with MLOps / LLMOps, model evaluation frameworks, and observability tooling.
  • Experience with fine-tuning, model distillation, or deploying SLMs on constrained / edge environments.
  • Familiarity with Agile methodologies and responsible / secure AI practices.
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