Company
Technology
TechLead/SolutionsArchitect(Python&GenAI)
Neural analysis suggests this role is
optimal for Senior candidates.
“TechLead / Solutions Architect (Python & GenAI). Skills: Python, GenAI, Cloud Architecture, LLM Applications. Write production-grade Python. Implement RAG systems”
Industry & Context.
Decomposing requirements; Architectural trade-off decisions; Troubleshooting
What They're Looking For.
Must Have
7+ years building production systems, Python proficiency, Experience building RESTful APIs, Experience making architectural trade-off decisions, Testing practices, Experience with Docker, Experience with Kubernetes, Hands-on experience building production LLM-based applications, Hands-on experience with AWS, B2+ English
Nice to Have
Experience with Streamlit, Experience with Gradio, Modern Python tooling, CI/CD pipeline experience, Experience in Go, Experience in Node.js, Experience in Rust, Front-end experience, GCP considered
What You'll Do.
Write production-grade Python
Implement RAG systems
Design LLM-based solutions
Build LLM-based solutions
Design agentic AI solutions
Build agentic AI solutions
Address client business challenges
Own technical direction
Conduct discovery calls
Write technical proposals
Scope client engagements
Perform client-facing demos
Lead architecture reviews
Produce technical design documents
Contribute to standards
Build client relationships
Maintain client relationships
Act as technical advisor
Decompose requirements
Produce scoped delivery plans
Produce phased delivery plans
Optimize cloud architecture costs
Build production systems
Integrate AI/ML components
Optimize Python performance
Make architectural trade-off decisions
Defend architectural decisions
Implement testing practices
Write integration tests
Build production LLM applications
Build agentic workflows
Maintain AI/ML models
Deploy applications on AWS
Maintain applications on AWS
How You'll Work.
Team & Collaboration
Collaborating across teams; Multicultural teams; Client stakeholders
Communication Scope
Client-facing demos; Technical proposals; Technical design documents; Knowledge sharing; Client communication
Process & Methodology
Scoping, Phased delivery plans, Risk identification
Full Job Description
## What You’ll Do 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 ## What You’ll Bring 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 No
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