About this Solutions Architect (AI, Python/Data) role at Provectus
What You’ll Do:
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Design and build cloud-native data, LLM-based, and agentic AI solutions addressing real client business challenges
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Implement and optimize RAG systems for production use cases
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Build and maintain strong relationships with key customer stakeholders, acting as a trusted technical advisor.
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Support presales: discovery calls, technical proposals, scoping, and client-facing demos
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Own the technical direction of client engagements from discovery through delivery — the go-to authority for clients and the internal team
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Write clean, production-grade Python across AI integrations, backend services, and RESTful APIs
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Build and maintain ETL/ELT workflows using modern orchestration and distributed computing tools.
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Deploy ML and LLM-based solutions
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Implement MLOps, LLMOps, and AgentOps practices: CI/CD, automated testing, model monitoring, and experiment tracking.
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Lead architecture reviews, produce technical design documents, and contribute to standards
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Mentor engineers, lead code reviews, and share knowledge across the team.
What You’ll Bring:
AI & Python/ Data & Cloud
- 7+ years building and running production systems — not only demos and POCs
- Hands-on experience building production LLM-based applications and agentic workflows
- Experience in integrating AI/ML components into solutions
- 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
- Python skills: OOP, design patterns, clean architecture, and performance optimization
- Experience building RESTful APIs with FastAPI, Django REST, or Flask
- Experience in making and defending architectural trade-off decisions
- Experience with Docker and Kubernetes
- Hands-on experience with AWS (Bedrock AgentCore, Bedrock, Lambda, ECS, S3, SQS, ECR, or similar); GCP considered
- Understanding of CI/CD practices applied to ML and AI pipelines
- Familiarity with model monitoring, observability, and drift detection.
- AWS and Claude Code Certifications
- CI/CD pipeline experience (GitHub Actions, GitLab CI)
- Experience in an additional language (Go, Node.js, or Rust).
- Hands-on experience with Apache Spark, Apache Airflow, Kafkа
