Principal AI/ML Engineer

The Principal AI/ML Engineer is a senior individual contributor who sits at the intersection of deep engineering and technical delivery leadership. This is not a management role, but an amplifier role. You write serious production code, and you use that credibility to drive the sprint by unblocking engineers, making implementation calls, coordinating across teams, and ensuring that every technical dependency between your squad and the rest of the organization gets resolved fast.

You will report to the Senior Director, AI & Architecture and work closely with the AI Architect and AI Engineers. You are the engineering face to the engineering teams across the organization. You are the person who turns AI architecture and strategy into running production systems.

You provide technical leadership and deliver innovative, scalable solutions that leverage artificial intelligence and machine learning to meet complex business needs. This role balances technical excellence with feasibility, cost, maintainability, and ethical use of AI and data. You are an exceptional engineer who has grown into the technical leadership of sprint delivery without stepping into architecture ownership or people management.
The successful candidate has a strong track record of delivering production-grade systems, influencing architecture decisions, and partnering effectively with business and technical stakeholders. This role extends beyond hands-on development to include technical design, mentoring, cross-functional collaboration, and influencing architecture.

ACCOUNTABILITIES:

You'll Own End-to-end AI/ML Solution Delivery:

• Participate in all phases of the AI development lifecycle, including problem framing, data analysis, solution design, model or agent development, evaluation and testing, deployment, monitoring, iterative improvement, support.
• Own the technical execution of sprint deliverables from design through deployment
• Drive daily engineering momentum: run stand-ups from a technical lens, surface blockers early, and resolve them before they become delays
• Make implementation-level decisions confidently and quickly within the established architecture
• Review pull requests with a focus on correctness, performance, security, and long-term maintainability
• Ensure engineering work is aligned to acceptance criteria and Definition of Done including eval thresholds for AI features
• Design, build, and maintain reusable, scalable AI/ML systems, including model pipelines, feature engineering workflows, and inference services.
• Partner with technical and business teams to translate complex business problems into effective AI/ML solutions.
• Provide effort estimation, dependency analysis, and technical risk assessment for initiatives, epics, and complex features.
• Act as a face of the AI Engineering team to the rest of the organization.

Technical Leadership & Governance:

• Provide technical leadership and engineering guidance for AI/ML solutions, ensuring alignment with enterprise standards, security requirements, and ethical AI principles.
• Lead technical design reviews, influence architectural decisions, and set best practices for AI/ML development, deployment, and lifecycle management.
• Mentor and guide engineers and data scientists on AI/ML design patterns, model evaluation, performance optimization, and responsible AI practices.
• Communicate complex technical concepts, tradeoffs, and outcomes clearly to both technical and non-technical stakeholders.
• Ensure solutions meet regulatory compliance, security, and data governance requirements, including privacy-by-design and model risk management.
• Act as a trusted technical advisor to engineering leadership, technical, and business stakeholders
• Identify and resolve cross-team technical dependencies proactively, before they block sprint delivery
• Translate architecture decisions from the AI Architect into concrete, sprint-ready engineering tasks
• Partner with BSAs to pressure-test requirements for technical feasibility and surface AI-specific constraints early
• Represent the team in ARB reviews, technical design sessions, and cross-functional working groups when needed

Engineering Mindset & Environment:

• Move fast with rigor; you make implementation calls quickly, document your reasoning, and don't wait for perfect requirements before writing the first line of code.
• Owns outcomes, not tasks; You stay accountable until the feature is live, stable, and working as intended in production.
• Navigates ambiguity with structured thinking; you know when to explore multiple approaches, when to build a quick proof of concept to de-risk a decision, and when to slow down and get alignment before committing to production.
• Earns credibility through craft; your ability to lead technically comes from the quality of your engineering judgment and the code you write, not the title on your badge.
• Makes the people around you better; you share context generously, write code others learn from, and pull teammates through blockers without doing their work for them.

REQUIRED QUALIFICATIONS:
Knowledge of:

• Scalable architecture patterns for traditional ML, GenAI and Agentic AI.
• AWS experience with SageMaker, Textract, Bedrock, Agentcore etc
• Strong familiarity with multiple LLMs and embedding models (e.g., OpenAI, Anthropic, Meta, Google, Hugging Face).
• Proficiency in multiple vector databases for semantic search and contextual memory.
• MLOps and LLMOps practices, including CI/CD, model monitoring, versioning, drift detection, and governance.
• Prompt engineering and management practices, including prompt versioning, A/B testing of prompts, and experience with prompt management tools
• Eval frameworks like Promptfoo, DeepEval or equivalent
• AI/ML observability stacks such as Dynatrace, Weights & Biases, Langsmith or similar tools.

Required Skills and Abilities:

• Hands-on experience designing and building AI/ML solutions from prototype to production.
• Proven ability to drive technical delivery in an agile/sprint environment to keep engineering moving
• Exposure to MLOps practices: model versioning, experiment tracking, deployment pipelines
• Experience with MCP (Model Context Protocol), and Familiarity with A2A patterns, or emerging agentic AI frameworks
• Strong Python development skills, including frameworks and libraries for ML, GenAI, and Agentic AI best practices.
• Deep understanding of software engineering, including modular design, testing, version control (Git), and CI/CD pipelines.
• Proven track record of building and running PoCs to validate architecture and feasibility.
• Experience working in agile environments, participating in sprints and cross-functional delivery.
• Ability to communicate technical concepts clearly to a wide range of stakeholders.
• Eagerness and ability to quickly learn and apply new AI/ML and automation technologies.
• Demonstrated commitment to learning and applying emerging technologies responsibly.

Education and/or Experience:

• Bachelor's degree in computer science, Engineering, Data Science, or related field preferred. Equivalent experiences may be substituted.
• 7+ years of experience in engineering or architecture roles with combined AI/ML experiences.
• Demonstrated experience of building, deploying, or supporting traditional ML models and GenAI/ Agentic AI solutions in real-world environments.
• Experience working within modern AI development lifecycles and Agile or iterative delivery models.

PREFERRED QUALIFICATIONS: (Additional qualifications that may make a person even more effective in the role, but are not required for consideration)

• Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, or equivalent).
• Familiarity with enterprise platform integrations: Salesforce, ServiceNow, Oracle HCM, or similar
• Experience in healthcare, life sciences, or other regulated/HIPAA environments
• Experience in mission-driven or non-profit environments.