AI & ML Platform Lead

Position

We are looking for a highly skilled AI/ML Platform Lead to technically lead our AI & ML Platform Operations team. The ideal candidate will lead the development and maintenance of our enterprise AI & ML platform to serve AI & ML workloads across the group.

Key Responsibilities

Platform Development

• Architect and implement infrastructure platforms tailored for AI/ML workloads, with a focus on scaling private cloud environments to support high-throughput training, inference, and Agentic workflows and pipelines.
• Build the Core GenAI application platform powering generative AI use cases across all of the team.blue group.
• Design and develop scalable services and tools to support GPU-accelerated AI pipelines, leveraging Kubernetes, Python/Go, and observability frameworks.
• Build and drive adoption of emerging AI/LLMOPs technologies, integrating AI Agents, RAGs, and LLMs using MCP workflows to streamline automation, performance tuning, and large‑scale data insights.
• Drive cross-functional adoption of AI tooling by delivering reusable components and intuitive interfaces, increasing developer productivity through automation, metrics dashboards, and streamlined GenAI integrations.

Platform Strategy

• Set the technical roadmap for platform capabilities, balancing innovation with reliability and compliance.
• Ensure the platform adheres to emerging standards of security, fairness, and explainability for LLM systems.

Technical Leadership & Mentorship

• Mentor and collaborate with a multidisciplinary team of network engineers, automation engineers, AI and ML scientists, product managers, and multiple domain experts.
• Provide technical leadership in operationalizing LLMs in production, including latency management and prompt versioning.

Solution Development Support

• Support initiatives in Generative AI systems design, including Retrieval‑Augmented Generation (RAG), LLM fine‑tuning, semantic search, and multi‑modal data processing.
• Build and optimize ML platform systems for document understanding, vector‑based retrieval, and knowledge graph integration.

Ideal Profile

• 6+ years of hands‑on experience building AI & ML platforms and infrastructure, using DevOps, infrastructure tools like Kubernetes, Docker, and Terraform.
• Deep understanding of current AIOps & MLOps practices, deploying and monitoring real‑time inference models and ETL pipelines.
• Experience with LLM toolchains such as LangChain, LlamaIndex, or OpenAI APIs.
• Deep understanding of model inference optimization (e.g., quantization, ONNX, streaming).
• Knowledge of RAG systems, vector databases (e.g., Qdrant, Pinecone), LLM inference engines (e.g., vLLM, Tensor‑LLM, Ollama).
• Experience operationalizing LLMs in production, including latency management and prompt versioning (e.g., Llama, BERT, Whisper, etc).
• Proficiency in backend development with Python (leveraging frameworks like FastAPI), microservices architecture and event‑driven architecture (e.g., Kafka, SSE, WebSocket).
• Strong problem‑solving skills with excellent communication and documentation skills.
• Ability to work collaboratively with cross‑functional teams.

Right to Work

At any stage, please be prepared to provide proof of eligibility to work in the country you’re applying for. Unfortunately, we are unable to support relocation packages or sponsorship visas.