Lead Technical AI Development

We are seeking an AI Technical Development Lead to own the end-to-end technical delivery of our AI-enabled product. You will manage and scale a multidisciplinary development team, define and implement technical solutions, and contribute hands-on to AI coding, software development, and planning. This role blends technical leadership, architecture, and execution, ensuring we build reliable, scalable, and cost-effective systems that deliver measurable business value., Team Leadership and Scaling

• Lead, mentor, and grow a high-performing development team (frontend, backend, AI/ML, QA, DevOps).
• Establish engineering standards, code quality practices, and review processes.
• Plan capacity, hire strategically, onboard effectively, and develop career paths for team members.
• Facilitate cross-functional collaboration with Product, Design, and Operations.

Technical Solution Ownership

• Own the technical architecture and system design for AI-driven features and platform components.
• Evaluate build-vs-buy, select frameworks and services, and ensure scalability, reliability, and security.
• Drive API design, data modeling, integrations, and platform observability.
• Ensure compliance with security, privacy, and regulatory requirements.

AI Engineering and Coding

• Design and implement AI/LLM-powered features (prompt engineering, retrieval augmented generation, tool/agent orchestration, evaluation).
• Build and integrate model pipelines, vector search, and inference services; optimize for latency and cost.
• Prototype quickly, productionize responsibly, and set up automated evaluations and monitoring for model quality.
• Contribute hands-on to code where needed (AI workflows, backend services, automation scripts).

Delivery, Planning, and Process

• Translate product requirements into technical plans, milestones, and sprints.
• Implement agile practices, manage backlog, and ensure predictable delivery.
• Define and track engineering KPIs (DORA metrics, uptime, MTTR, cost per inference, model accuracy).
• Coordinate releases, change management, and incident response.

Quality, Security, and Reliability

• Champion testing at all levels (unit, integration, E2E, AI evals), CI/CD, and dev/test parity.
• Implement observability (logs, metrics, tracing) and data quality checks.
• Partner with DevOps to ensure robust infrastructure, backups, and disaster recovery.
• Conduct architecture and security reviews; enforce best practices and documentation., * Collaborate with Product to refine requirements and define measurable outcomes.
• Lead sprint planning, backlog prioritization, and technical reviews.
• Balance hands-on contributions with strategic leadership and team enablement.
• Foster an engineering culture focused on clarity, reliability, and continuous improvement.

Success Metrics (First 6-12 Months)

• Predictable delivery with clear milestones and improved cycle time.
• Stable, scalable architecture with clear observability and incident response.
• Shipped AI features with measurable business impact (accuracy, latency, user adoption, cost per inference).
• A cohesive, high-performing team with strong engineering practices and hiring pipeline in place.

Tools and Environment (examples; adaptable to your stack)

• Code: TypeScript/Node.js, Python
• Web/App: React/Next.js
• Data: PostgreSQL, vector DB (e.g., pgvector, Supabase)
• Infra: Docker, CI/CD (GitHub Actions), Azure
• AI: OpenAI/Anthropic, Qdrant, LangChain/LlamaIndex, evaluation frameworks
• Project: Jira/Linear, GitHub/GitLab, Notion/Confluence

Why Join

• Lead the technical vision and execution of AI-first products.
• Combine leadership with hands-on AI development.
• Build and scale a modern engineering organization with real product impact.
• Participation program

Must Have

• Proven experience leading software development teams and delivering production systems.
• Strong system design and architecture skills across web applications, APIs, databases, and cloud services.
• Hands-on experience building AI/LLM features (prompt design, RAG, vector databases, evaluation, and monitoring).
• Proficiency in at least two relevant languages (e.g., Python, TypeScript) and modern frameworks.
• Solid understanding of CI/CD, testing strategies, containerization (Docker), and cloud (AWS/GCP/Azure).
• Excellent planning, communication, and stakeholder management skills.

Nice to Have

• Experience with multi-tenant SaaS architectures and high-scale systems.
• Knowledge of Next.js/React, Node.js, GraphQL/REST, Postgres, and message queues.
• Familiarity with MLOps, feature stores, and model lifecycle management.
• Exposure to cost optimization, FinOps, and data privacy/compliance (GDPR, SOC 2).
• Experience integrating AI agents, workflow orchestration, or tools like LangChain, LlamaIndex.