Principal AI/ML Engineer - USA

Cogniify•Scottsdale, AZ

112d•$135,900 - $213,100•Remote

About The Position

We are seeking a Principal AI/ML Engineer to serve as a technical leader and architect for our organization’s AI/ML strategy, systems, and platforms. In this role, you will define the long-term technical vision for machine learning and MLOps, drive cross-team alignment on architecture and standards, solve the hardest technical problems, and ensure our ML capabilities are enterprise-grade, scalable, and forward-looking. The ideal candidate is a recognized technical authority who combines deep hands-on expertise with strategic thinking, organizational influence, and the ability to elevate the entire engineering team.

Requirements

Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field preferred. Equivalent professional experience is accepted.
10+ years of professional experience in software engineering, ML engineering, or applied AI, with at least 5 years focused on production ML systems.
Deep expertise in designing and scaling production ML platforms, pipelines, and infrastructure.
Authoritative knowledge of MLOps principles, tools, and practices including CI/CD for ML, automated retraining, model governance, and observability.
Extensive experience with cloud-native ML services across AWS, Azure, or GCP, and infrastructure-as-code tools (Terraform, Pulumi, CloudFormation).
Strong understanding of distributed systems, data architecture, and scalable platform design.
Proven track record of driving technical strategy and influencing engineering direction at an organizational level.
Experience leading and mentoring senior engineers and building high-performing technical teams.
Exceptional communication skills with the ability to align technical and business stakeholders on complex topics.
Strong understanding of responsible AI, model fairness, explainability, security, and compliance requirements.

Nice To Haves

Experience architecting LLM-based systems, RAG pipelines, agentic AI platforms, or conversational AI at enterprise scale.
Experience with MCP/tool-layer integrations for LLM-driven systems.
Deep familiarity with feature platforms, model serving at scale, and real-time inference architectures.
Track record of contributing to or leading industry standards, publications, or open-source projects in AI/ML.
Experience with enterprise AI governance frameworks and regulatory compliance (SOC2, HIPAA, GDPR).
Experience working across geographically distributed or offshore engineering teams.
Background in financial services, healthcare, or other highly regulated industries.

Responsibilities

Define and own the long-term technical vision, architecture, and roadmap for the organization’s AI/ML platform and MLOps capabilities.
Serve as the senior technical authority on ML system design, providing guidance on architecture, tooling, and engineering standards across the organization.
Lead the design of foundational ML infrastructure including training platforms, feature stores, model registries, serving systems, and observability frameworks.
Establish and evangelize best practices for the full ML lifecycle: experimentation, development, testing, deployment, monitoring, governance, and retirement.
Drive cross-functional alignment between ML engineering, data engineering, platform engineering, product, and security teams.
Evaluate emerging technologies, frameworks, and research to inform strategic decisions on AI/ML tooling and approaches.
Solve complex, ambiguous, and high-impact technical problems that span multiple teams or systems.
Mentor and develop senior and staff-level engineers, fostering a culture of technical excellence and continuous improvement.
Represent the engineering organization in discussions with leadership, partners, and clients on AI/ML capabilities and strategy.
Define and enforce governance, compliance, and responsible AI practices across ML systems.
Drive cost optimization and efficiency across ML training, serving, and infrastructure.
Contribute to hiring, technical assessments, and the growth of the AI/ML engineering team.