Lead ML/AI Engineer: Build Production-Grade AI for Health

Requirements

• Advanced Python proficiency; solid experience with ML frameworks (Tensor

  Flow, Py

  Torch, scikit-learn)
,
• Strong SQL skills: complex queries, performance tuning, data modeling basics
,
• Generative AI expertise: LLM APIs (Open

  AI, Claude,…), Lang

  Chain/Llama

  Index
,
• MLOps experience: CI/CD pipelines, model monitoring, deployment at scale
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• Cloud platform experience (AWS/GCP/Azure) and managed ML services (SageMaker, Bedrock, Vertex AI)
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• Product mindset with a bias for measurable impact and ROI
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• Clear communication with non-technical partners; ability to write crisp documentation (ex: diving / specifications / exploration / problem statement / …)
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• Strong ownership and autonomy; pragmatic problem-solving approach
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• Collaborative spirit with Product, Design, and Engineering teams; embraces feedback culture

What the job involves

• As the first Senior ML/AI Engineer at Hublo, you will help us build our AI and Machine Learning capabilities from the ground up
,
• We are at the beginning of this journey, and your primary mandate is to identify which problems are truly worth solving with AI and ML
,
• As part of the Data Platform team, you will explore, prototype, and ship intelligent features powered by ML and LLMs that deliver measurable value to healthcare professionals
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• You'll work closely with Product to identify high-impact AI opportunities, then partner with both Engineering and Data teams to build, deploy, and maintain production solutions
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• This role directly addresses a core challenge: reducing administrative friction for caregivers and health managers so they can spend more time on patient care but only where AI proves to be the right solution
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• Own end-to-end delivery of AI/ML features, from problem framing to production deployment and iteration
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• Cover the full ML/AI scope: classical ML (recommendations, predictions, optimization) and LLM-based features (assistants, document understanding, search)
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• Run disciplined POCs with clear success metrics, baseline comparisons, and go/no-go criteria defined upfront with Product
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• Make pragmatic technical decisions on modeling approaches, data requirements, evaluation methods, and build vs buy trade-offs
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• Kill what doesn't work: document learnings from failed experiments and redirect resources quickly
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• Ensure production quality: latency, reliability, observability, security, and graceful degradation when models fail
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• Set initial standards for ML/LLM engineering: experiment tracking, prompt/model versioning, evaluation harnesses, and documentation templates
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• Build reusable components where it unblocks velocity: shared datasets, template pipelines, monitoring dashboards, keep it pragmatic
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• Lay the groundwork for MLOps/LLMOps: CI/CD for models, A/B testing infrastructure, basic drift/quality monitoring
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• Document "how we do AI at Hublo": evaluation rules, production checklists, and safety guidelines
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• Work closely with Product to identify high-impact AI use cases, shape scope based on feasibility, and align on success metrics
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• Partner with Engineering on integration, performance requirements, and operational reliability, own the AI/ML part, collaborate on the rest
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• Communicate clearly on uncertainty: especially for LLM limitations, expected quality, and trade-offs, set realistic expectations early
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• Lead and animate the AI Community of Practice: drive discussions, share patterns and learnings, ensure it stays active and valuable for the company
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• Grow AI literacy with short, practical sessions: what works when, how to evaluate AI outputs, common pitfalls to avoid
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• Share openly: write postmortems (including failures), document technical decisions, and make your work reusable
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• Encourage evidence over hype: measurable outcomes, honest limitations, and realistic timelines, set the tone for how Hublo builds AI
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• Uplift on key user outcomes from AI features (e.g., +X% reduced task duration, −Y% time-to-action)
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• Feature adoption and retention for AI-powered workflows (weekly active users, repeat usage)
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• Quality gains: recommendation precision/recall, summarization quality scores, CSAT on AI features
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• Model and service latency and availability within agreed SLAs
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• Drift and incident rate kept below threshold; time-to-recovery after issues
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• Time from prototype to production; cadence of meaningful iterations per quarter
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• Experiment throughput with clear learnings (A/B tests, offline evaluations → shipped features)