Key skills
PythonAIMLGenAIAgenticMLOpsGitVersion ControlPerformance OptimizationAgileCI/CDLeadershipRisk Management
About this role
Role Overview
- 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.
- 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.
Requirements
- 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.
- 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.
Tech Stack
- Python
Benefits
- medical, dental, vision, life and disability, accident/critical illness/hospital, well-being, legal, identity theft and pet benefits.
- Retirement
- paid time off/holidays
- leave
- incentive plans