Lead AI Engineer

Sunovaa Tech Pvt. Ltd.; Bengaluru; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Siemens Healthineers is seeking an experienced AI Engineer / AI Platform Architect to lead adoption, architecture and optimisation of enterprise Generative AI solutions. Strong focus on AI coding assistants, Code LLM platforms, and governance across cloud and on-prem environments.

The ideal candidate will demonstrate hands-on expertise with LLMs, RAG, AI platform engineering, cloud architecture, DevOps, and FinOps to optimise token usage and costs.

Qualifications

• Hands-on implementation and architecture experience in Generative AI.
• Proven track record delivering enterprise-grade AI solutions at scale.
• Bachelor's or Master's in CS, Engineering, IT or related field.

Responsibilities

• Design, deploy and manage enterprise Generative AI and LLM platforms.
• Implement and optimise RAG, embeddings and vector databases.
• Evaluate AI coding assistants such as GitHub Copilot, Claude Code or OpenCode.
• Develop architectures using Azure OpenAI, Azure AI Foundry and cloud services.
• Build data pipelines and dashboards tracking AI adoption and KPIs.
• Implement AI FinOps to optimise token usage and infra costs.
• Ensure security, governance, compliance, scalability and reliability.
• Integrate AI platforms with GitHub, Azure DevOps and CI/CD pipelines.
• Provide technical leadership and mentor engineering teams.

Job description

Job Title: AI Engineer Generative AI & Code Intelligence Platform

Company: Siemens Healthineers
Experience: 10+ Years
Required Relevant Experience: 5+ Years in AI/ML or Generative AI
Job Function: Engineering – Software & QA / AI & Machine Learning
Employment Type: Full-time

Job Summary

We are looking for a highly experienced AI Engineer / AI Platform Architect to lead the adoption, architecture, administration and optimisation of enterprise Generative AI solutions, with a strong focus on AI coding assistants, Code LLM platforms, Agentic AI and enterprise AI platforms.

The ideal candidate will have strong hands-on experience in LLMs, Retrieval-Augmented Generation (RAG), AI platform engineering, cloud architecture, DevOps, AI governance and cost optimisation.

Key Responsibilities

• Design, deploy and manage enterprise Generative AI and LLM platforms.
• Implement and optimise RAG, GraphRAG, vector search and Agentic AI solutions.
• Evaluate and administer AI coding assistants such as GitHub Copilot, Claude Code, OpenCode or equivalent platforms.
• Develop enterprise AI architectures using Azure OpenAI, Azure AI Foundry and other cloud AI services.
• Build automated data pipelines and dashboards to track AI adoption, usage, productivity and operational KPIs.
• Implement AI FinOps practices to optimise token consumption, licensing and infrastructure costs.
• Ensure enterprise AI security, governance, compliance, scalability and reliability.
• Integrate AI platforms with GitHub, Azure DevOps, CI/CD pipelines and enterprise SDLC tools.
• Provide technical leadership, develop architecture standards and mentor engineering teams.

Mandatory Technical Skills

• Generative AI, LLMs and foundation models
• Retrieval-Augmented Generation (RAG), embeddings and vector databases
• Agentic AI and frameworks such as LangChain, LangGraph or Semantic Kernel
• Azure OpenAI, Azure AI Foundry or equivalent enterprise AI platforms
• Python, REST APIs and automation
• Cloud architecture, preferably Azure, including hybrid or multi-cloud environments
• Kubernetes, Docker, CI/CD, DevSecOps and Infrastructure as Code
• Enterprise AI platform integration, governance, security and monitoring
• Strong architecture, stakeholder management and technical leadership skills

Preferred Skills

• GitHub Copilot, Claude Code, OpenCode or equivalent AI coding assistants
• GraphRAG, Knowledge Graphs and Neo4j
• Databricks, Apache Spark, Kafka and lakehouse platforms
• Power BI or equivalent dashboarding tools
• AI FinOps, token optimisation and AI usage analytics
• Responsible AI frameworks and enterprise AI Centre of Excellence initiatives

Experience & Qualifications

• 10+ years of overall experience in enterprise IT, software engineering, platform engineering or technical architecture.
• 5+ years of hands-on experience designing, deploying and operating AI/ML or Generative AI solutions at enterprise scale.
• Proven experience delivering enterprise-grade AI solutions across complex cloud or hybrid environments.
• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, AI or a related discipline.

Candidates should demonstrate hands-on implementation and architecture experience, rather than only theoretical knowledge of Generative AI.