Lead Applied AI Engineer II

Responsibilities

• Outcome driven accountability: uphold a culture focused on customer and business outcomes, delivering engineering solutions that solve complex problems with measurable value while maintaining lean, high‑quality designs and implementations.
• Technical leadership and advocacy: act as the product’s technical champion, validating code quality and feasibility and aligning work with business and customer goals. lead requirements analysis, low‑level architecture and component design, development, testing, integrations, and support.
• Engineering craftsmanship: safeguard architecture integrity and the enterprise standards across the tech stack. manage dependencies, design, implementation, quality, data, and ongoing maintenance. remain hands-on, continuously learn new approaches, and create technical specifications; produce high‑quality, scalable code and review peers’ code while mentoring them to ensure quality.
• Customer centric engineering: develop lean solutions through rapid, low‑cost experimentation to address customer needs. engage with customers and product teams before, during, and after delivery to ensure the right solution is delivered at the right time.
• Incremental and iterative delivery: adopt a forward‑leaning mindset that emphasizes action and evidence over heavy planning, delivering lean, maintainable solutions that navigate complexity.
• Cross-functional collaboration and integration: work with empowered product management, experience, and delivery teams; integrate diverse perspectives to balance feasibility, viability, usability, and value, fostering a collaborative environment that drives innovation.
• Advanced technical proficiency: demonstrate deep expertise in modern software engineering practices, including AI and Agentic SSDLC, delivering daily product deployments with full automation from discovery to production to operations and quality checks throughout the SSDLC lifecycle. be a role model and apply these techniques to optimize solutioning and delivery, while focusing on the full lifecycle of product development and continuous improvement.
• Domain expertise: rapidly acquire domain knowledge relevant to the business or product; translate business needs, architectures, and UX/UI designs into technical specifications and code; contribute as a flexible, quality‑driven teammate mindful of tech debt payoff.
• Effective communication and influence: communicate complex technical concepts clearly and persuasively, inspiring teammates and product teams through well‑structured arguments and evidence‑based trade‑offs; craft narratives that align technical solutions with business objectives.
• Engagement and collaborative co‑creation: collaborate with product engineering teams at all levels, including customers when needed; build constructive relationships and foster a culture of co‑creation and shared momentum toward product goals; align diverse perspectives to reach feasible solutions.

Requirements

• Bachelor’s degree in computer science, software engineering, data science, machine learning, or a related discipline.
• Six or more years of experience with technologies such as Angular, React, NodeJS, Python, C#, .NET, Java, SQL/NoSQL, PyTorch, TensorFlow, LangChain, LangGraph, and relevant unit testing frameworks.
• Three or more years building AI/ML applications, with hands-on GenAI experience across LLM integration (OpenAI, Anthropic, or open‑source models), RAG pipelines, prompt engineering, and vector databases.
• Three or more years of cloud‑native engineering using FaaS, PaaS, or microservices on Azure, AWS, or GCP, including AI/ML services such as Azure OpenAI, AWS Bedrock, or Vertex AI.
• One or more years establishing engineering standards, including actively leading, mentoring, and guiding team members in adopting and improving these standards.
• Prior software engineering experience with Business Context Diagrams, sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, code instrumentation, and AI augmented, spec‑driven development.
• Experience using XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, MLflow, and agentic AI frameworks (for example LangFuse, LangSmith, or similar multi‑agent orchestration tools) to deliver high quality products rapidly.

Technologies

• Angular, React, NodeJS, Python, C#, .NET, Java
• SQL/NoSQL, PyTorch, TensorFlow
• LangChain, LangGraph
• OpenAI, Anthropic, LangFuse, LangSmith
• Azure, AWS, GCP; Azure OpenAI, AWS Bedrock, Vertex AI
• FaaS, PaaS, MLflow
• ADO, GitHub, SonarQube

The Successful Candidate Will Possess

• Excellent interpersonal and organizational skills, with the ability to handle diverse situations, manage complex projects, and adapt to changing priorities with professionalism and empathy.

Other

• Travel availability of approximately 10 percent, depending on project requirements.
• Limited immigration sponsorship may be available.
• The stated wage range reflects a broad range of factors considered in compensation decisions, including skill, experience, and business needs; geographic differentials may apply.
• You may be eligible for a discretionary annual incentive program, subject to program rules and individual and organizational performance.