Senior Solution Architect (AI / LLM solutions) in Hyderabad, भारत

ZF is a global technology company supplying advanced mobility products and systems for passenger cars, commercial vehicles and industrial technology. Its comprehensive product range is primarily aimed at vehicle manufacturers, mobility providers and start-up companies in the fields of transportation and mobility. ZF electrifies a wide range of vehicle types. With its products, the company contributes to reducing emissions, protecting the climate as well as enhancing safe mobility. Alongside the automotive sector – passenger cars and commercial vehicles – ZF also serves market segments such as construction and agricultural machinery, wind power, marine propulsion, rail drives and test systems. With some 168,700 employees worldwide, ZF reported sales of €46.6 billion in fiscal year 2023. The company operates 162 production locations in 31 countries. About the Team We are expanding our AI Engineering organization at ZF and are hiring Senior Solution Architects. Our team builds enterprise-grade AI platforms and solutions across Agentic AI, Generative AI for the software development lifecycle, systems engineering, automated testing, MLOps, computer vision, and engineering data intelligence. Our work includes developing AI agents for requirements engineering, code development, test generation, and traceability. We also address complex automotive engineering challenges, including virtual sensors, lateral and longitudinal control, perception engineering, object detection and tracking, digital twins, condition monitoring, and intelligent validation. What you can look forward to as Solution Architect: AI/ML (m/f/d)

• End-to-end ownership: You'll own the technical architecture of enterprise Generative AI and Agentic AI solutions, from framing the business problem through to secure, observable and cost-efficient production systems.
• Real-world impact: You'll turn LLM capabilities into reliable, auditable solutions that hold up against complex enterprise data, strict security and compliance requirements, tight latency and cost budgets, and unpredictable model behavior. This role goes well beyond proof of concept.
• Architecture leadership: You'll set the reference architecture, platform patterns and guardrails that multiple engineering teams build on.
• Strategic decisions: You'll lead decisions on what to build versus buy, which models to use, and whether to customize models through fine-tuning, weighing quality, cost, scalability and vendor independence.
• Cutting-edge technology: You'll design multi-agent systems on leading frameworks and cloud platforms, such as LangGraph, AWS Bedrock and Azure AI Foundry.
• Visibility and influence: You'll be the trusted technical authority for business leaders, security teams and enterprise architecture boards.

• A bachelor’s or master’s degree in computer science, Engineering, AI or a related field.
• 10+ years in software, data or ML engineering, including at least 3 years as a solution or lead architect.
• Production GenAI delivery: You have delivered at least 2–3 LLM / Generative AI solutions into production, not only proofs of concept, and owned the architecture and non-functional requirements end to end.
• Agentic AI: You have built hands-on agentic systems hands-on, with tool use, state management and human-in-the-loop controls. You've used code-first frameworks such as LangGraph / LangChain or managed platforms such as AWS Bedrock Agents or Azure AI Foundry Agent Service.
• LLM and model expertise: You have a strong grasp of LLM fundamentals (transformers, embeddings, context windows). You have hands-on experience customising models through fine-tuning (e.g., LoRA / QLoRA) and evaluating them, and you can choose sensibly between prompting, RAG and training.
• Engineering and cloud: You are an expert in Python and API engineering (FastAPI, async, event-driven design). You have strong architecture experience on at least one hyperscaler (AWS, Azure or GCP), including networking, identity and security.
• RAG, quality and security: You have practical experience with vector databases, hybrid search and retrieval evaluation. You've implemented LLM evaluation, tracing and observability in production, and you're well-grounded in AI security (prompt-injection defence, data protection).