Lead Generative AI Engineer

• Engineering and deploying production‑ready generative AI solutions, including LLMs, VLMs, and multimodal models, with a strong emphasis on inference, scalability, and reliability.
• Designing and operating LLM Ops pipelines, including model versioning, fine‑tuning, evaluation, deployment, rollback, and lifecycle management.
• Building and maintaining AI platforms and services that support prompt management, embeddings, vector search, retrieval‑augmented generation (RAG), and tool‑calling workflows.
• Integrating generative AI capabilities into enterprise applications using APIs, microservices, and event‑driven architectures.
• Implementing MLOps best practices, including CI/CD for models, automated testing, performance benchmarking, observability, logging, and cost monitoring.
• Optimizing model performance across latency, throughput, accuracy, and cost using techniques such as quantization, catching, batching, and model routing.
• Collaborating with cloud, data, security, and product teams to ensure solutions meet enterprise standards for security, governance, and responsible AI.
• Producing clear technical documentation and operational runbooks and communicating delivery status and business value to stakeholders.
• Mentoring engineers and contributing to reusable frameworks, standards, and platform capabilities.

Fuel Your Passion

To be successful in this role, you will have:

• A master’s degree in computer science, AI, Machine Learning, or a related field, or equivalent hands‑on industry experience.

• PhD is a plus, but strong delivery experience is preferred.
• Proven experience deploying and operating generative AI models in production, rather than only research or experimentation.
• Strong proficiency in Python, with practical experience using PyTorch, TensorFlow, Hugging Face, and transformer‑based architectures.
• Experience with AI platform and MLOps tooling, such as model registries, experiment tracking, orchestration, CI/CD pipelines, and monitoring solutions.
• Solid understanding of cloud‑native architectures, containers, and scalable inference patterns (e.g., Kubernetes‑based deployments).
• Hands‑on experience with RAG systems, vector databases, embeddings, prompt optimization, and evaluation frameworks.
• Strong software engineering discipline, including testing, code reviews, documentation, and production support.
• Excellent problem‑solving, collaboration, and communication skills, with the ability to work effectively across engineering and business teams.
  • A delivery‑focused mindset, comfortable-owning systems in production and continuously improving them.