What youll be doing:
Work closely with engineers, managers, and cross-functional teams to understand complex engineering domains, identify bottlenecks, and build practical solutions that improve decision-making, execution speed, and operational quality.
Develop and deliver production-grade tools, automated workflows, and agentic AI solutions from concept through deployment.
Run fast, high-quality proof-of-concepts on emerging AI and agentic technologies, and productize successful ones.
Implement data flywheels that continuously improve quality through telemetry, benchmarking, automated evaluation, and structured feedback loops.
Collaborate and contribute ideas and code to Product Engineering's evolving data infrastructure.
Improve data quality, integrity, governance, metric definitions, and usability across engineering domains.
Train and enable engineers, managers, and stakeholders to use data, BI, and AI tools effectively.
What we need to see:
B.Sc or M.Sc in Computer Science, or related field, or equivalent experience.
12+ years of proven experience building and deploying production software, data products, internal tools, or engineering productivity platforms and 2+ years of experience building AI-enabled or agentic systems, including tools & skills, RAG pipelines, persistent memory, and evaluation infrastructure.
Hands-on development experience with Python, full-stack software development, SQL and NoSQL databases, cloud environments, and internal tool development.
Extensive experience utilizing coding agents for development.
A proactive, high-agency builder who deciphers complex domains to deliver pragmatic, production-grade AI and data solutions that drive measurable engineering productivity. An adaptable expert who masters the intersection of software engineering and agentic AI, taking full ownership of the lifecycle from messy data debugging to cross-functional leadership.
Excellent collaboration skills, with the ability to influence cross-functional partners, build positive relationships, and communicate complex concepts clearly to both technical and business audiences.
Demonstrated commitment to continuous learning and development.
Ways to stand out from the crowd:
Background in product engineering, hardware engineering, networking, semiconductors, or complex engineering organizations/
Evidence of meaningful open-source contributions, including core commits, maintainership, widely adopted libraries, or public technical artifacts demonstrating system-level depth.
