Senior Machine Learning Engineer

Required Skills & Experience • Education: Master's degree in Computer Science, Machine Learning, Data Science, or a related field. A Bachelor's degree with relevant experience will also be considered. • Experience: 2–4 years of experience building and deploying ML or AI systems in production. Experience working directly with non-technical stakeholders or in embedded/consulting-style engineering roles is a strong plus. • Technical Skills: Strong proficiency in Python. Experience with LLM APIs, agentic frameworks (LangChain, Strands, etc.), and prompt engineering alongside traditional ML frameworks (PyTorch, scikit-learn, etc.). Solid software engineering fundamentals — version control, testing, CI/CD, and comfort operating across the full development lifecycle. • Healthcare Knowledge: Interest in or familiarity with healthcare data, clinical workflows, and regulatory requirements. Experience working with electronic health records (EHR) or other healthcare datasets is a plus but not required. • Analytical Skills: Strong problem-solving skills and the ability to work with complex datasets to derive actionable insights. • Communication: Excellent verbal and written communication skills, with the ability to explain technical concepts to non-technical stakeholders. • Builder Mindset: Energized by turning ideas into working solutions. You balance speed with quality, thrive in ambiguous problem spaces, and pick up new domains quickly. • Team Player: Ability to work collaboratively in a cross-functional team environment, accept feedback, and contribute to the success of the team. Job Description As a Machine Learning Engineer, you will design, build, and ship AI agents and automation that solve real problems across engineering, product, and delivery organizations, including customer-facing operations. You'll partner directly with stakeholders across Engineering, Product, and healthcare professionals to understand their workflows, identify high-leverage opportunities, and deliver working solutions end-to-end. Your growing expertise in machine learning and agentic AI will have a direct impact on how the client builds software, delivers for customers, and operates at scale. Key Responsibilities 1. AI Platform Development: Develop and implement AI Agents and automation that accelerates internal engineering workflows and customer facing delivery processes, owning the full lifecycle from problem discovery, through prototyping, evaluation, hardening, and production deployment. Contribute reusable libraries, prompt templates, tool-use patterns, and evaluation scaffolding back to the AI Platform. 1. Integration: Partner with software engineers to integrate AI into the company's existing software infrastructure, supporting seamless functionality and performance. 1. Collaboration: Work directly with product managers, implementation consultants, engineers, and business operations teams to identify pain points, scope solutions, and iterate toward measurable outcomes. You are the bridge between what AI can do and what the business needs done. 1. Research and Learning: Stay current with advancements in LLMs, agentic frameworks, machine learning, and healthcare technology, and apply new knowledge to contribute ideas for innovation within the team. 1. Performance and Reliability: Optimize AI systems for accuracy, latency, cost, and safety, with particular attention to human-in-the-loop design and guardrails appropriate for healthcare. 1. Documentation: Maintain clear documentation of model development processes, methodologies, and results to ensure transparency and reproducibility. Rate: 85-95/hr W2