AI / ML Engineering Lead

Job Description

This is a remote position.

As the Engineering Lead, you’ll architect and build the technical backbone of our clients' healthcare product.

You’ll work closely with our ML developers to deploy image classification models and nutrition recommendation engines into production. You’ll also lead the development of our expert and patient-facing apps, build scalable infrastructure, and set the technical standards for everything from model inference to compliance and security.

Responsibilities

• Own the AI integration pipeline from training to deployment—working hand-in-hand with ML researchers to ship systems in production.
• Build and scale model-serving infrastructure for multimodal AI (CV, nutrition, future LLMs).
• Implement API endpoints and backend logic to support real-time and asynchronous inference, leveraging frameworks like FastAPI or LangChain where appropriate.
• Lead development of frontends for clinical experts and patients, prioritising performance and simplicity.
• Set up and manage cloud infrastructure (e.g. AWS, GCP) for model serving, API management, analytics, and user access controls.
• Collaborate with product and design to turn ML outputs into intuitive features (e.g., diagnosis suggestions, treatment plan builders).
• Ensure AI-related privacy, security, and auditability—aligned with GDPR / HIPAA standards.
• Establish strong engineering practices and scale the team through mentorship and hiring.

Requirements

Must-Have Qualifications

Nice-to-Haves

Benefits

What You Get

Requirements

Python Programming PyTorch / TensorFlow / Transformers Frameworks Machine Learning Pipeline Development Large Language Models (LLMs) & API Integration Retrieval-Augmented Generation (RAG) & Vector Databases Agent Orchestration & Model Context Protocol (MCP) MLOps & CI / CD for Model Deployment Cloud AI Platforms (AWS SageMaker / Azure ML / GCP Vertex AI) Model Optimization (Quantization, Distillation, RLHF) Data Security & Compliance (SOC 2 / NIST / CJIS)