Python/GenAI Solutions Architect

Accountabilities

• • Embed directly with client teams throughout engagements, working within their environments and collaborating closely with the people whose workflows the technology is designed to improve.
  • Conduct discovery with end users and stakeholders to understand operational processes, pain points, and requirements before defining technical solutions.
  • Develop clean, maintainable, production-grade Python across AI integrations, backend services, and RESTful APIs using frameworks such as Flask, Django REST, or FastAPI.
  • Design, build, deploy, and optimize production RAG systems and agentic AI solutions, ensuring they move beyond prototypes into reliable enterprise applications.
  • Own system architecture and technical decisions across engagements, evaluating approaches such as microservices versus monoliths, synchronous versus event-driven architectures, and SQL versus NoSQL databases.
  • Lead the technical direction of projects from initial discovery through implementation, deployment, optimization, and production support.
  • Serve as the primary technical contact for clients, presenting architecture, explaining trade-offs, managing expectations, and challenging scope when requirements conflict with timelines, budgets, or technical realities.
  • Support presales activities when appropriate, including discovery sessions, technical proposals, solution scoping, cost estimation, and client-facing demonstrations.
  • Lead architecture reviews and create detailed technical design documentation, engineering standards, and reusable implementation patterns.
  • Contribute proven approaches and lessons learned to internal blueprint libraries and delivery frameworks to improve future engagements.
  • Mentor engineers, lead code reviews, promote engineering best practices, and share technical knowledge across the broader Python and AI engineering community.
  • Evaluate AI system quality and reliability through appropriate testing, monitoring, evaluation, and quality assurance practices.
  • Help ensure AI/ML systems remain performant, maintainable, secure, and reliable after deployment.

  Requirements

  • 7+ years of experience building and operating production software systems, with substantial hands-on engineering experience. Production experience is essential; demo- or proof-of-concept-only experience is not sufficient.
  • Demonstrated production experience designing and operating RAG systems and LLM-based agentic workflows.
  • Strong Python development expertise, including object-oriented programming, design patterns, clean architecture, performance optimization, and maintainable software design.
  • Professional experience with backend frameworks such as Flask, Django REST, or FastAPI.
  • Recent hands-on experience with AWS services such as SageMaker, Bedrock, Lambda, ECS, S3, SQS, ECR, or comparable cloud technologies. GCP experience is also considered.
  • Experience integrating LLM APIs such as OpenAI, Anthropic Claude, or AWS Bedrock.
  • Demonstrated ability to make, communicate, and defend system architecture and technical trade-off decisions.
  • Comfortable communicating directly with client stakeholders and independently leading technical conversations without relying on a project manager to act as an intermediary.
  • Genuine interest in understanding users and operational workflows before designing technology solutions, including the ability to work effectively when engagements begin with incomplete or evolving requirements.
  • Strong software testing practices, including pytest, mocking, integration testing, and testing approaches specific to AI systems.
  • Experience with Docker and Kubernetes.
  • Understanding of LLM evaluation techniques, AI quality assurance, and methods for assessing the reliability and effectiveness of AI-powered systems.
  • Experience deploying, operating, and maintaining AI/ML models in production environments.
  • Regular use of AI-assisted development tools such as Claude Code, GitHub Copilot, or comparable tools.
  • Proactive, self-directed approach with strong ownership of technical outcomes from discovery through production.
  • Strong problem-solving skills and the ability to identify and address issues before they become delivery blockers.
  • B2+ English proficiency and the ability to collaborate effectively with distributed, multicultural teams.
  • Exposure to Financial Services or Healthcare and Life Sciences is a plus.
  • Presales experience involving cost estimation, cloud architecture optimization, delivery scoping, or phased implementation planning is preferred.
  • Previous consulting, professional services, or embedded client-facing delivery experience is advantageous.
  • Experience with React or Vue is a plus.
  • AWS or Claude Code certifications are beneficial.
  • Experience with Streamlit or Gradio for AI prototyping is a plus.
  • Familiarity with modern Python tooling such as ruff, uv, pyproject.toml, and pyright is desirable.
  • Experience with CI/CD platforms such as GitHub Actions or GitLab CI is beneficial.
  • Experience with an additional programming language such as Go, Node.js, or Rust is a plus.

  Benefits

  • Remote-friendly working environment.
  • Opportunity to work within a growing AI delivery practice and help develop new tools, frameworks, and engineering approaches.
  • High-impact position with direct visibility to senior leadership.
  • Strong earning potential with performance-based bonuses.
  • Opportunity to work hands-on with cutting-edge Generative AI, cloud, and software engineering technologies.
  • Flexible engagement options, including B2B contract or full-time employment models.
  • Unlimited vacation policy.
  • Generous health, vision, and dental insurance.
  • 401(k) matching plan.
  • Opportunity to work directly with enterprise clients and influence the adoption of production-grade AI solutions.
  • Professional growth through technical leadership, mentoring, architecture ownership, and exposure to diverse client engagements.