• Design and optimize RAG pipelines over domain-specific content • Improve retrieval quality, ranking, and grounding to reduce hallucinations • Build evaluation frameworks to measure accuracy and consistency • Work with fragmented enterprise data and make it usable • Build AI-powered features integrated into STARLIMS workflows • Design for reliability (latency, scale, model variability) • Implement guardrails, fallbacks, and observability • Manage prompt evolution, model drift, and regressions • Develop systems that can reason, call tools, and execute multi-step tasks • Integrate with internal APIs, developer tooling, and external systems • Build and operate backend services on AWS (Lambda, API Gateway, DynamoDB, etc.) • Own system architecture and key technical decisions • Contribute to infrastructure-as-code and deployment pipelines

Role Description We are seeking experienced AI Engineers to support a public sector healthcare initiative, delivering production-grade AI solutions in a highly regulated environment. This role focuses on real-world impact, building systems that operate within strict privacy, security, and governance frameworks. This is not a research role—you will be responsible for deploying and operationalizing AI in production within an enterprise Azure ecosystem. What You’ll Be Doing in the Role - Design, build, and deploy production AI/ML solutions in Azure - Work with Azure ML / Azure AI services across the full lifecycle (training, deployment, monitoring, retraining) - Integrate models into secure enterprise systems (APIs, backend services, data pipelines) - Handle healthcare and regulated data, ensuring compliance with privacy and security standards - Collaborate with data engineers on large-scale data platforms (e.g., Databricks, ETL pipelines) - Implement responsible AI practices, including explainability, bias mitigation, and auditability - Work within public sector delivery structures, engaging with architects, developers, and non-technical stakeholders Qualifications - Proven experience delivering AI/ML solutions in production environments - Hands-on experience with Azure ML / Azure AI (strongly preferred) - Experience working with regulated data environments (healthcare, government, or similar) - Strong understanding of model deployment, monitoring, and lifecycle management - Experience integrating AI into operational systems, not just building models - Ability to work in structured, compliance-driven environments Nice to Have - Experience with clinical/NLP data or healthcare analytics - Familiarity with FHIR and healthcare interoperability standards - Exposure to population health, risk modeling, or patient outcomes use cases - Experience implementing GenAI/LLM solutions in secure enterprise environments And You Are... - Always improving your skills and knowledge: you want to be the best at what you do - Curious and creative; comfortable taking on new problems and challenges - A self-starter with the ability to recommend priorities to the project leader - Comfortable working through problems and figuring things out with minimal supervision - Great problem-solving skills: thorough and reliable - Able to work in a team: share knowledge and assist other team members - A good communicator: able to explain your ideas and recommendations - Well organized and dependable under pressure: you manage your time effectively - Energized by our company values! Benefits - A values-driven workplace where people really matter. - Flexible work location - Support for remote work/work from home. - Competitive salary, retirement savings program, and rewards program - Comprehensive health, dental, vision, life and disability insurance plans and access to e-health care - Paid vacation, maternity/parental leave, paid sick leave and paid Mariner MyLife days. - Unlimited training

• Own the architecture on AI engagements • Serve as a credible technical voice with client stakeholders • Run workshops and design reviews • Architect agentic systems, RAG pipelines, and AI-augmented workflows • Contribute to reference architectures, skills, prompts, and MCP servers • Help build out our growing OpenAI and Anthropic practices • Pair with senior architects and engineers on the hard parts • Translate between business goals and engineering reality • Write clearly, present crisply, disagree well • Contribute to internal IP.

• Assist in the design, development, testing, and deployment of AI/ML models and applications • Collaborate with Engineering, IT, and business stakeholders to translate business needs into technical solutions • Build and optimize data pipelines and inference workflows • Develop prototypes and AI-powered applications within cloud environments such as AWS • Work with both structured and unstructured datasets, including text, APIs, and image-based data • Experiment with large language models (LLMs), prompt engineering techniques, and AI APIs • Document code, experiments, and technical findings for team collaboration and knowledge sharing • Participate in code reviews, sprint planning, and technical discussions with the Engineering team • Present project work and findings to technical and business stakeholders • Support additional projects and initiatives as needed across the department