This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Python/GenAI Solutions Architect based in United States.
This role is designed for a senior engineer or solutions architect who combines deep Python expertise with hands-on Generative AI delivery experience.
You will work directly within client environments, translating real operational needs into production-ready AI systems rather than proof-of-concept solutions.
The position spans technical discovery, architecture, software development, deployment, optimization, and ongoing delivery ownership.
You will design and build RAG systems, agentic AI workflows, backend services, and cloud-native architectures that operate at enterprise scale.
Direct client engagement is central to the role, giving you responsibility for explaining technical decisions, challenging assumptions, and aligning solutions with business outcomes.
You will also contribute to presales, architecture standards, reusable delivery frameworks, and the development of other engineers.
This is an opportunity to shape how modern AI solutions are delivered while working with advanced cloud and Generative AI technologies.
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.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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