Senior Solutions Architect | Python and GenAI

• 7+ years building and running production systems; hands on production experience is required, demo only or POC only backgrounds will not be considered

• Production experience with RAG systems and LLM based agentic workflows that are live and running, not prototypes

• Backend development experience with Flask, Django REST, or FastAPI

• Recent, hands on experience with AWS (SageMaker, Bedrock, Lambda, ECS, S3, SQS, ECR, or similar); GCP considered

• Experience with LLM APIs (OpenAI, Anthropic Claude, or AWS Bedrock)

• Strong Python proficiency: OOP, design patterns, clean architecture, and performance optimization

• Demonstrated experience making and defending system design and architectural trade off decisions

• Comfortable owning direct communication with client stakeholders, without a project manager relaying the conversation

• Genuine willingness to spend time close to the people who do the job before you design a system to change it, and comfort with an engagement that starts underspecified by design

• Strong testing practices: pytest, mocking, and integration tests for AI systems

• Experience with Docker and Kubernetes

• Understanding of LLM evaluation techniques and quality assurance approaches

• Experience deploying and maintaining AI/ML models in production environments

• Already using AI assisted development 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 are handed to you

• B2+ English, comfortable collaborating across distributed, multicultural teams

• Exposure to Financial Services or Healthcare and Life Sciences, our two primary practice verticals

• Presales experience: cost estimation, cloud architecture cost optimization, scoped and phased delivery plans

• Prior consulting, professional services, or other embedded client facing delivery

• Experience with React or Vue

• AWS or Claude Code certifications

• 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)

• Remote-friendly culture

• Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance

• Career growth; we actively develop our engineers

• Access to the latest AI tools and premium subscriptions

• Long-term B2B collaboration

• Private medical insurance or a budget for your medical needs

• Paid sick leave, vacation, and public holidays

• Equipment and all the tech you need for comfortable, productive work

We are changing how we deliver. Instead of handing a client a design and waiting for someone else to build it, our engineers embed directly inside the client's team and ship the system themselves. We call this Forward Deployed delivery. Before you design anything, you spend real time with the people who do the work the system is meant to change, so the architecture reflects how the work actually happens, not how a requirements document described it.

We are looking for a Senior Software Engineer or Solutions Architect with deep Python and Generative AI experience who wants to work this way: deep in the code, close to the client, and directly accountable for whether the system gets used. Direct client communication sits at the center of this role. Prior presales experience is a strong plus.

• Embed with the client team for the length of the engagement, working inside their environment rather than alongside it from a distance

• Spend time with the people who do the job the system is meant to change before you design anything, so the architecture is grounded in how the work actually happens

• Write clean, production grade Python across AI integrations, backend services, and RESTful APIs (Flask, Django, or FastAPI)

• Design, build, and optimize RAG systems and agentic AI solutions running in production, not demos or notebooks

• Own system design and architectural decisions across the engagement: microservices vs monolith, sync vs event driven, SQL vs NoSQL

• Own the technical direction of the engagement from discovery through delivery

• Act as the client's direct technical point of contact: present architecture, defend trade offs, and push back on scope when it does not match timeline or budget

• Support presales activities where relevant, including discovery calls, technical proposals, scoping, and client facing demos

• Lead architecture reviews, produce technical design documents, and contribute to standards across the Python practice

• Feed what you learn back into Provectus's blueprint library, so the next engagement starts further ahead

• Mentor engineers, lead code reviews, and share knowledge across the team