Senior AI Engineer

Senior AI Engineer needed to design and ship AI products for a healthcare company that enables remote patient monitoring, clinical research, and personalized healthcare. You'll build generative-AI workflows, clinical decision support, agents, and retrieval over health data — along with the evaluation and guardrails that make them safe to use in a clinical setting. You'll own these products end to end and work across teams, partnering closely with clinicians, product, and engineering to identify where AI genuinely improves a patient or clinician outcome — and explaining the trade-offs clearly to both technical and non-technical people. This is a hands-on senior role for an AI builder with strong software fundamentals who wants to own the outcome of an AI product, not just the code.

What You'll Do:

• Take AI products from problem framing to production — scope the use case, choose the approach (generative AI, classical ML, retrieval, agents, or a hybrid), and ship it
• Design prompts, tools, and retrieval strategies that work reliably in a clinical context, with structured outputs and clear failure modes
• Build the services and APIs that expose AI capabilities to our products, with real attention to latency, cost, and observability
• Shape the data behind each product — sources, schemas, labelling, and quality checks — and build the pipelines that prepare it for training, evaluation, and inference
• Define how quality is measured (offline metrics, golden sets, human review, and live monitoring) and use the results to drive iteration
• Build guardrails, content checks, and graceful degradation paths suited to healthcare use cases
• Operate AI products in production — monitor model behaviour, catch drift and regressions, and run incident response when needed
• Write production-quality code, tests, and documentation, review others' work to the same standard, and use AI-assisted development tools to move faster without compromising quality
• Partner with clinical, product, and engineering colleagues to decide where AI is — and isn't — the right answer, keeping everyone aligned and clearly communicating AI trade-offs to technical and non-technical stakeholders

• Mentor other engineers on applied AI — evaluation, generative-AI design patterns, and shipping models responsibly

   

What We're Looking For:

• 5+ years of professional software engineering experience, with meaningful time building and shipping AI/ML products in production
• A track record of owning an AI product from problem statement to launch — including model/approach selection, evaluation, and iteration based on real usage
• Hands-on experience with generative-AI systems: prompting, tool/function calling, retrieval-augmented generation (RAG), multi-step agents, structured output, and handling failure modes
• Solid grounding in core machine learning (supervised learning, embeddings, evaluation, overfitting, data quality), with the judgment to know when to use classical ML, fine-tuning, or off-the-shelf models — and when AI isn't the answer at all
• Strong production engineering skills — clean, well-tested code, sensible API design, and the ability to operate your own services (Python is our default)
• Comfort with the data side of an AI product — ingesting, cleaning, and modelling structured and unstructured data, and designing pipelines for training, evaluation, and inference
• An evaluation mindset — defining offline and online metrics, building golden sets or human-review loops, and catching regressions before users do
• Clear, confident written and verbal communication — you can explain AI trade-offs to an engineering stand-up and to a clinical or executive stakeholder in the same day, and adapt your style to each
• An ownership and product mindset — you care whether the model and the product are doing the right thing for users, you stay accountable after launch, and you balance capability, cost, latency, safety, and complexity for the stage of the product
• Confident with Git, code review, automated testing, and CI/CD

Nice to Have:

• Production experience with vector databases, embedding pipelines, and retrieval-quality tuning
• MLOps practices — model versioning, evaluation harnesses, shadow deployments, and prompt/dataset management
• Experience with cloud AI platforms (Azure AI, AWS Bedrock/SageMaker, GCP Vertex) and deploying inference on AWS, GCP, or Azure
• Familiarity with fine-tuning, distillation, or hosting open-weight models when economics or privacy call for it
• Background in healthcare, life sciences, or another regulated, data-sensitive domain (HIPAA, GDPR, ISO 27001, medical-device software)
• Experience with FHIR, HL7, or other healthcare interoperability standards

What Success Looks Like:

• AI products that ship and make a measurable difference — adoption, task completion, and clinical or operational outcomes where defined
• Healthy AI behaviour in production — strong evaluation scores, a high regression catch rate, and safety incidents avoided
• Reliable, observable AI services with healthy uptime, latency, and cost for the surfaces you own
• Predictable delivery within agreed scope and timelines
• Engineers you mentor or pair with growing and becoming more effective