Lead AI Engineer
- Compensation
- ₹2.5M–₹3.3M / yr
- Location
- Bengaluru
- Work setup
- Hybrid
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Ai engineering
- Posted
Skills
Nice to have
- LangChain, LangGraph, AutoGen, CrewAI or Semantic Kernel.
- Azure, AWS, Kubernetes, Docker and Infrastructure as Code.
- Vector databases, embeddings, GraphRAG and Knowledge Graphs.
- Power BI, AI adoption analytics and AI FinOps.
- Enterprise AI governance and Responsible AI frameworks.
- Experience establishing AI Centres of Excellence or enterprise AI platforms.
Full description
AI Engineer Generative AI & Code Intelligence Platform
Experience: 10+ years overall IT experience, including 5+ years in AI/ML or Generative AI
Department: Engineering – Software & AI
Location: As per Siemens requirement
Job Summary
We are looking for an experienced AI Engineer / AI Platform Architect to lead the adoption, architecture, governance and optimisation of enterprise AI solutions, focusing on Generative AI, Large Language Models (LLMs), AI-powered coding assistants and Agentic AI.
The role involves designing scalable and secure AI platforms, integrating AI into software development workflows, establishing enterprise AI governance and optimising platform usage and costs across cloud and hybrid environments.
Key Responsibilities
- Administer and manage enterprise AI coding platforms such as GitHub Copilot, Claude Code, OpenCode and self-hosted LLMs.
- Design, deploy and operate scalable enterprise AI platforms.
- Develop Generative AI solutions using LLMs, RAG, GraphRAG, embeddings, vector databases and Agentic AI.
- Evaluate foundation models, AI frameworks, vector stores and emerging AI development tools.
- Design enterprise AI architectures across cloud, hybrid and multi-cloud environments.
- Integrate AI platforms with GitHub, Azure DevOps, CI/CD pipelines and enterprise SDLC toolchains.
- Develop AI usage analytics, adoption metrics and productivity dashboards using Power BI or equivalent tools.
- Establish AI FinOps practices to optimise token usage, licensing, compute utilisation and cloud costs.
- Implement AI security, access management, governance and Responsible AI practices.
- Collaborate with enterprise architecture, security, platform engineering and development teams.
- Provide technical leadership, mentoring, architecture standards and AI enablement programmes.
Mandatory Skills
- Generative AI and Large Language Models (LLMs).
- Retrieval-Augmented Generation (RAG) and Agentic AI.
- Enterprise AI platform engineering and architecture.
- AI coding assistants such as GitHub Copilot, Claude Code or equivalent.
- Cloud AI platforms such as Azure AI Foundry or Azure OpenAI.
- Python, REST APIs and automation.
- Cloud architecture, DevOps, CI/CD and security fundamentals.
Preferred Skills
- LangChain, LangGraph, AutoGen, CrewAI or Semantic Kernel.
- Azure, AWS, Kubernetes, Docker and Infrastructure as Code.
- Vector databases, embeddings, GraphRAG and Knowledge Graphs.