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Lead AI Engineer

ID da vaga
499111
Publicado desde
17-Mar-2026
Área de trabalho
Research & Development
Empresa
Siemens Healthcare Private Limited
Nível de experiência
Profissional Experiente
Anúncio da vaga
Tempo Integral
Modo de trabalho
Apenas escritório/presencial
Tipo de contrato
Permanente
Localização
  • Bangalore - - Índia
Job Purpose
We are seeking a Lead AI Engineer with 6–10 years of experience in artificial intelligence, machine learning, and generative AI to lead the design and delivery of enterprise-grade AI solutions. The ideal candidate will have deep expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agent architectures, along with experience deploying scalable AI systems in cloud-native environments.

In this role, you will architect, build, and scale advanced AI capabilities using modern AI platforms such as Azure OpenAI, Anthropic, and other LLM services, enabling innovation across SHS R&D digital platforms. You will also provide technical leadership, mentorship, and architectural guidance to engineering teams developing AI-powered products and solutions.

Key Responsibilities

AI Solution Architecture
• Design and architect enterprise-grade generative AI solutions, including RAG pipelines, knowledge retrieval systems, and agent-based AI workflows.
• Define scalable AI architecture patterns for integrating LLM capabilities into internal platforms and applications.
• Lead the design of AI-driven digital solutions that align with enterprise architecture, security, and compliance requirements.

LLM & Generative AI Engineering
• Lead the development and optimization of LLM-powered applications using technologies such as Azure OpenAI, Anthropic Claude, and other foundation models.
• Architect and implement RAG pipelines, semantic search, vector databases, and knowledge-grounded AI systems.
• Design AI agent orchestration workflows for complex enterprise automation scenarios.

Platform Engineering & Scalability
• Build scalable, production-grade AI systems with robust monitoring, evaluation, and performance optimization.
• Implement MLOps/LLMOps practices, including model lifecycle management, prompt evaluation, observability, and CI/CD integration.
• Optimize AI systems for latency, cost, and reliability in production environments.


Technical Leadership
• Provide technical leadership and architectural direction for AI initiatives across multiple teams.
• Mentor junior AI engineers and data scientists in best practices for LLM engineering, prompt engineering, and AI system design.
• Conduct technical reviews, architecture validations, and design consultations for AI initiatives.

Research & Innovation
• Continuously evaluate emerging advancements in Generative AI, NLP, multi-agent systems, and foundation models.
• Drive innovation initiatives, prototypes, and proof-of-concepts to explore new AI capabilities relevant to R&D workflows.
• Identify opportunities to leverage AI for productivity, automation, and knowledge management across engineering teams.

Experience
• 6–10 years of experience in AI/ML engineering, data science, or AI platform development.
• Hands-on experience with Generative AI and Large Language Models (LLMs) in production environments.
• Strong experience designing RAG architectures, vector search systems, and AI agents.
• Experience building AI-powered applications and services using frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar.
• Experience deploying AI workloads in cloud environments (Azure, AWS, or GCP).
• Experience designing scalable distributed systems and microservice-based architectures.

Required Knowledge, Skills, Education, and Experience
Education
• BS/MS in Computer Science, Artificial Intelligence, Data Science, Information Technology, or related field.

Technical Skills
• Strong programming expertise in Python (preferred), Java, or similar languages.
• Experience with deep learning frameworks such as PyTorch or TensorFlow.
• Experience with vector databases and semantic search technologies (e.g., Pinecone, Weaviate, FAISS, Azure AI Search).
• Strong understanding of LLM orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel).
• Experience implementing AI agent architectures and multi-step reasoning workflows.

Cloud & Infrastructure
• Hands-on experience with cloud-native AI deployment on Azure, AWS, or GCP.
• Experience working with containerization and orchestration technologies (Docker, Kubernetes).
• Familiarity with data pipelines, distributed computing frameworks, and high-performance environments (Databricks, Apache Spark).

Additional Skills
• Strong understanding of AI evaluation, model monitoring, and AI governance.
• Ability to translate business requirements into scalable AI architectures.
• Excellent collaboration skills with business leaders, key experts, data scientists, software engineers, product teams, and stakeholders, at a global setup.