You will also act as the primary owner of architecture, infrastructure, release pipelines, and risk/health tracking, ensuring our products achieve industry-best standards for scalability, reliability, and innovation.

This role requires strong technical leadership, deep expertise in modern deployment pipelines, and Python and AI/ML, contributing directly to the company's product and technology strategy.

Key Responsibilities:

• Lead, coach, and mentor engineers (ML, full-stack, and QA) across two squads, driving delivery and technical excellence.
• Champion best practices in engineering, including code quality, testing, observability, and security.
• Own the overall software architecture for both platforms, ensuring scalability, security, and high availability.
• Oversee application and infrastructure design, cloud environments, and release/production pipelines.
• Ensure adherence to best practices in SaaS product engineering, CI/CD, observability, and DevOps/MLOps.
• Build a culture of ownership, accountability, and continuous improvement.
• Guide teams working with LLMs, transcription pipelines, embeddings, and ML-driven workflows.
• Ensure models, services, and APIs are production-ready, performant, and compliant.
• Cross-functional alignment with Product and Design.
• Manage technical debt, prioritize effectively, and de-risk engineering roadmaps.
• Track platform reliability, uptime, latency, and cost efficiency.
• Define and monitor engineering metrics (e.g., DORA, QA pass rates, SLA adherence).
• Identify and mitigate risks across infrastructure, data, and compliance.

Requirements:

• 10 years of professional experience in software engineering, with increasing leadership responsibilities.
• Prior hands-on background in backend or full-stack development, plus strong understanding of ML/AI systems and LLM integration.
• Hands-on experience working with ML models, data pipelines, and inference systems.
• Proven track record of leading AI/ML-focused engineering teams and shipping production SaaS products.
• Strong command of system design, distributed systems, and SaaS architecture.
• Proficiency with cloud platforms (AWS, GCP, or Azure), CI/CD pipelines, observability tools, and DevOps practices.
• Understanding of AI/ML workflows (training, inference, evaluation, deployment) and experience with LLMs, NLP, and transcription models.
• Familiarity with modern frontend/backend frameworks (React, Node.js, Python).
• Excellent ability to guide, train, and measure engineering performance.
• Strong communication and collaboration skills with the ability to operate autonomously in cross-functional squads.
• Domain expertise in AI SaaS products, with the ability to challenge and set technical direction confidently.
• Strategic mindset: able to balance short-term delivery with long-term scalability and reliability.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.