Lead AI Software Engineer

Tricentis•Austin, TX

About The Position

Key Responsibilities Technical Leadership & Team Enablement Act as the technical lead for one or more teams, setting engineering direction and ensuring high technical standards. Provide hands-on architectural guidance, unblocking complex technical challenges, and reviewing critical designs. Mentor Senior and Mid-level Engineers through structured coaching, design reviews, and ongoing technical feedback. Drive and facilitate technical decision-making, including trade-offs between scalability, reliability, cost, and time-to-market. Lead and contribute to lunch & learns, technical deep dives, and internal knowledge-sharing initiatives. Partner with Engineering Managers and Product Leaders to translate business goals into executable technical strategies. Proactively identify gaps in team capability and influence hiring, onboarding, and skill development strategies. Architecture, Implementation & Quality Lead rapid experimentation initiatives, ensuring learnings are clearly documented and communicated to stakeholders. Define and standardize testing strategies for AI systems, including unit, integration, regression, and evaluation-based testing. Ensure consistency and quality across implementations through code reviews, architectural reviews, and reference implementations. Embed deeply in business domains, proactively shaping product direction through technical insight. Identify systemic technical issues and drive long-term, sustainable solutions rather than short-term fixes. Champion continuous improvement in engineering practices, tooling, and workflows.

Requirements

6+ years of experience with Python in production environments.
3+ years of experience designing, deploying, and operating language model–based solutions in production.
Strong experience utilizing AI coding Assistants (Github Copilot, Cursor, Claude Code) in daily workflow.
Working knowledge of emerging AI technologies like MCP, A2A, and GenAI LLM’s.
Proven ability to develop systems that balance innovation with reliability and maintainability.
Expert-level understanding of software engineering best practices, including architecture patterns, CI/CD, testing, and code quality.
Experience designing and operating data pipelines and data management systems at scale.
Strong understanding of security, privacy, and compliance considerations in AI-enabled systems.
Extensive experience with containers and orchestration in production environments.
Ability to diagnose and resolve complex production issues involving AI systems.
Exceptional ability to communicate complex technical concepts clearly to engineers, product leaders, and non-technical stakeholders.
Comfortable acting as the technical voice of the team in cross-functional discussions.
Ability to influence without authority and align the team around a shared technical vision.
Comfortable collaborating across teams to enable your teams’ delivery.

Responsibilities

Act as the technical lead for one or more teams, setting engineering direction and ensuring high technical standards.
Provide hands-on architectural guidance, unblocking complex technical challenges, and reviewing critical designs.
Mentor Senior and Mid-level Engineers through structured coaching, design reviews, and ongoing technical feedback.
Drive and facilitate technical decision-making, including trade-offs between scalability, reliability, cost, and time-to-market.
Lead and contribute to lunch & learns, technical deep dives, and internal knowledge-sharing initiatives.
Partner with Engineering Managers and Product Leaders to translate business goals into executable technical strategies.
Proactively identify gaps in team capability and influence hiring, onboarding, and skill development strategies.
Lead rapid experimentation initiatives, ensuring learnings are clearly documented and communicated to stakeholders.
Define and standardize testing strategies for AI systems, including unit, integration, regression, and evaluation-based testing.
Ensure consistency and quality across implementations through code reviews, architectural reviews, and reference implementations.
Embed deeply in business domains, proactively shaping product direction through technical insight.
Identify systemic technical issues and drive long-term, sustainable solutions rather than short-term fixes.
Champion continuous improvement in engineering practices, tooling, and workflows.