About the role

Vice President of Engineering overseeing technical strategy and AI solution deployment at Gentoro. Driving engineering excellence within a fast-paced startup environment while fostering team growth.

Responsibilities

Define and execute the company’s technical roadmap, ensuring alignment with product goals and business objectives. Translate product vision into a robust and scalable engineering strategy.
Lead the development and maintenance of seamless, robust integrations with major enterprise platforms.
Oversee the development, testing, and production deployment of agentic solutions. Implement best practices for MLOps, monitoring, and performance tuning of AI models in live environments.
Serve as a leader who actively engages with the codebase and delivery process. This includes leading delivery sprints, performing code reviews, and actively involved in developing proofs of concept (POCs) to validate architectural decisions and set technical standards for the team.
Recruit, mentor, and retain a distributed engineering team. Foster an engineering culture that balances rapid delivery, operational excellence, collaboration, and continuous learning.
Establish SDLC processes, automate CI/CD pipelines, elevate QA standards, and develop incident response protocols to enable high velocity, availability and reliability of our platform
Navigate the inherent ambiguity and resource constraints of a startup environment. Make pragmatic trade-offs between speed, quality, and technical debt.

10+ years of software product development experience, include 5+ years of engineering leadership experience at a fast-paced, high-growth technology startup from early stage through Series A/B funding (or equivalent growth phase)
Build a high-performing team by hiring well, investing in people's growth, and creating conditions where strong engineers thrive.
Deep technical expertise in building and managing enterprise-level integrations, APIs, and data synchronization across complex B2B software ecosystems.
Proven experience building and deploying AI agents into production environments, including familiarity with MLOps principles and challenges specific to AI systems.
Experience leveraging or implementing RAG or MCP to integrate AI agent interactions with external tools and data sources is a strong advantage.
Strong architectural background, proficient in modern technologies(e.g., Python, semantic search, vector DBs, GraphQL, queues, containers, Kubernetes, real-time data processing, Spark, Open Telemetry, Clickhouse).
Ability to switch effortlessly between solving immediate technical challenges and shaping long-term technical direction
Exceptional ownership skills driving on-time delivery of high-quality code in production while fostering a collaborative environment where engineers feel empowered to share diverse perspectives on solutions