About The Role:

Provectus is a global AI and cloud consulting company helping enterprises turn artificial intelligence and data into production-ready business solutions. We specialize in designing, building, and scaling end-to-end AI/ML systems, data platforms, and cloud-native architectures, with strong expertise in AWS, MLOps, and enterprise-grade AI delivery.
We are an official Anthropic partner, working with cutting-edge foundation models to help organizations safely and effectively adopt advanced AI capabilities.
Our consulting teams operate across industries such as finance, healthcare, retail, and technology, delivering solutions with measurable business impact through hands-on engineering and advisory.
As a Technical Lead, you will drive the development of GenAI-powered solutions, including AI agents, RAG systems, and Python services. You will provide technical leadership, own solution architecture, mentor engineers, and guide projects from discovery to production.

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 NoSQL

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

Experience with Docker and Kubernetes

Hands-on experience building production LLM-based applications and agentic workflows

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

Hands-on experience with AWS (SageMaker, Bedrock, Lambda, ECS, S3, SQS, ECR, or similar); GCP considered

Experience with React/Vue

AWS and Claude Code Certifications

Nice to Have

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)

Front-end experience

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
.