Senior Data Scientist

**NO SPONSORSHIP AVAILABLE**

Principal Machine Learning Engineer (Technical Strategy & Architecture) -

Must Haves

• 10+ years of overall experience in technology, with early career experience as a software developer.
• 5+ years of experience within machine learning or artificial intelligence.
• Strong foundation in Java and/or Python, with the ability to read, assess, and reason about legacy code.
• Experience with or willingness to learn Go LANG and other newer software languages.
• Advanced experience in Machine Learning, AI, or Generative AI, including architectural and system-level design.
• Proven ability to make technical decisions at scale without direct ownership of daily coding tasks.
• Deep understanding of backend engineering principles, system design, and data-driven architectures.
• Demonstrated ability to operate as a technical leader without people management responsibilities.

Day to Day

This role is designed for a deeply technical, architecture-focused leader who sets technical direction across ML and AI initiatives without direct people management responsibilities.

This individual will be the strongest technical leader in the room, responsible for understanding complex legacy systems, defining the future technical vision, and driving proof of concept (POC) work that informs long-term platform decisions. While this role is not hands-on with day-to-day coding, it requires exceptional technical depth and decision-making capability.

• Serve as the principal technical authority for Machine Learning and GenAI initiatives.
• Define and communicate the technical roadmap, architectural direction, and long-term AI strategy.
• Evaluate legacy systems and platforms, identifying technical constraints, risks, and modernization paths.
• Make high-impact technical decisions that guide engineering teams and influence platform evolution.
• Lead and design POCs to validate new Machine Learning and GenAI approaches, tools, and architectures.
• Assess feasibility, scalability, and integration considerations for AI solutions before broader adoption.
• Translate experimental results into clear architectural recommendations and next steps.
• Deeply understand existing backend systems, data flows, APIs, and legacy codebases.
• Bridge the gap between historical system design and future-state AI/ML capabilities.
• Partner closely with engineering teams to ensure alignment between strategic decisions and implementation realities.

Additional details:

• ML/AI architecture and strategic decision-making across legacy and modern platform stacks
• Design and delivery of LLM, RAG, hybrid retrieval, and agentic workflow patterns in production-oriented systems
• Strong backend and systems engineering fluency, including APIs, data flows, integration constraints, and modernization tradeoffs
• Practical fluency in core engineering languages and tools, especially Python and Java, with working familiarity with Go, shell scripting, Git/GitHub, Docker, Kubernetes, Helm, and CI/CD workflows
• Working knowledge of cloud engineering and deployment patterns, ideally including AWS and EKS-based environments and GitOps workflows
• Strong working understanding of experimentation, evaluation, benchmarking, and how model/AI outcomes inform architectural and product decisions
• Technical leadership across engineering, product, and platform teams, with clear communication and execution in complex environments