Python/GenAI Tech Lead

• Write clean, production-grade Python across AI integrations, backend services, and RESTful APIs. • Implement and optimize RAG systems for production use cases. • Design and build LLM-based and agentic AI solutions that address real client business challenges. • Own the technical direction of client engagements from discovery through delivery. • Support presales: discovery calls, technical proposals, scoping, and client-facing demos. • Lead architecture reviews, produce technical design documents, and contribute to standards across the Python practice. • Mentor engineers, lead code reviews, and share knowledge across the team. • Build and maintain strong relationships with key client stakeholders as a trusted technical advisor.
Mindset • Full-stack mindset, comfortable across AI, backend development, and cloud infrastructure. • Already using AI tools in your daily workflow (Claude Code, Copilot, or similar). • Proactive and self-directed; you own outcomes end-to-end and spot problems before they're handed to you. • B2+ English, comfortable collaborating across distributed, multicultural teams. Presales & Client Engagement • Owns the client technical relationship; leading discovery, decomposing ambiguous requirements into technical components, presenting architecture, and pushing back on scope when it doesn't match timeline or budget. • Produces scoped, phased delivery plans with clear deliverables, dependencies, and risks. • Experience with cost estimation and cloud architecture cost optimization. Python, AI & Cloud • 7+ years building and running production systems, not only demos and POCs. • Strong understanding of AI/ML concepts and experience integrating AI/ML components into solutions. • Strong Python proficiency: OOP, design patterns, clean architecture, and performance optimization. • Experience building RESTful APIs with FastAPI, Django REST, or Flask. • Experience making and defending architectural trade-off decisions: microservices vs monolith, sync vs event-driven, SQL vs NoSQL. • Strong testing practices: pytest, mocking, and integration tests for AI systems. • Experience with Docker and Kubernetes. • Hands-on experience building production LLM-based applications and agentic workflows. • Experience with LLM APIs (OpenAI, Anthropic, or AWS Bedrock). • Experience building and optimizing RAG systems. • Understanding of LLM evaluation techniques and quality assurance approaches. • Experience deploying and maintaining AI/ML models in production environments. • Hands-on experience with AWS (SageMaker, Bedrock, Lambda, ECS, S3, SQS, ECR, or similar); GCP considered. • Experience with React/Vue. • AWS and Claude Code Certifications. Nice to Have • Experience with Streamlit or Gradio for AI prototyping. • Modern Python tooling (ruff, uv, pyproject.toml, pyright). • CI/CD pipeline experience (GitHub Actions, GitLab CI). • Experience in an additional language (Go, Node.js, or Rust). • Front-end experience.
• Opportunity to work with cutting-edge AI and cloud solutions. • Career growth: a clear path toward SA or beyond — we actively develop our engineers. • Access to the latest AI tools and premium subscriptions. • Remote with flexible hours. • Long-term B2B collaboration; • A budget for your medical insurance; • Paid sick leave, vacation, public holidays; • Continuous learning support, including unlimited AWS certification sponsorship.