* Provectus is an **AWS Premier Partner** and an **Anthropic Strategic Partner**, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide. * Our work centers on two verticals — **Financial Services \& Insurance** and **Healthcare \& Life Sciences** — where we deploy five pre\-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture. * Our team holds 100\+ AWS certifications, is Claude Code certified, and co\-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements. **Where this role sits** ------------------------ You will work in a senior pod alongside an FDE, an FDX, and Senior AI Engineers — contributing to real delivery work while building toward independent ownership. ### **Requirements:** **Mindset** * Proactive and self\-directed; you push for clarity rather than waiting for a ticket. * Excellent communication and problem\-solving skills. * Comfortable with some ambiguity, with support from senior team members as you take on more. * B2\+ English, comfortable collaborating across distributed, multicultural teams. **Technical depth** * Hands\-on experience building or contributing to RAG systems, ideally in a production or near\-production setting. * Solid engineering fundamentals; Python and/or TypeScript proficiency. Productive in an unfamiliar codebase with some ramp\-up support. * Practical AWS experience (Lambda, S3, ECS, or similar); ready to grow into Bedrock and Bedrock AgentCore . * Some experience with containers and CI/CD in real projects. * Exposure to evaluating non\-deterministic systems — you've contributed to or run test/eval cycles, even if you haven't owned a full eval suite end\-to\-end. * Basic working knowledge of model/agent monitoring concepts. * Awareness of cost and latency trade\-offs when working with LLMs. * Some hands\-on exposure to the Claude ecosystem (Claude Code, CLAUDE.md, hooks, skills files) is a plus, or strong ability to ramp up quickly. * Practical experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) in real projects. * 2\+ years of software or ML engineering experience, including some exposure to production systems. * Solid AI/ML foundations — you understand what the models do well enough to reason about common failure modes. ### **Nice to Have:** * Experience in one of the industries: financial services, insurance, healthcare. * Consulting, professional services, or other embedded customer\-facing delivery. * AWS and Claude Code Certifications (or actively pursuing them). * A2A: Interest in agent\-to\-agent interoperability concepts. * CI/CD pipeline experience (GitHub Actions, GitLab CI). * Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines. * Experience in an additional language (Go, TypeScript, or Rust). * Experience with Apache Spark, Apache Airflow, Kafkа. * Experience with the Claude ecosystem — Claude Code, CLAUDE.md, hooks, skills files. Spec\-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build. * MCP: you can say why an agent would prefer it to a REST integration, having authored a server is an additional plus. ### **Responsibilities:** * Build and contribute to RAG system components under senior guidance, with growing autonomy. * Write tests and help build out evaluation harnesses for the features you work on. * Write production code across the stack (AI, backend services, data pipelines) with code review support. * Help integrate AI components into backend services and RESTful APIs. * Support deployment of systems to AWS (containerized, CI/CD), taking on more of this independently over time. * Contribute to documentation, runbooks, and client handover materials. * Participate in technical discussions and architectural decisions, with an eye toward taking on more of this independently. * Support model evaluation efforts and help investigate and improve failure modes. * Take on increasing ownership of components and technical decisions as you grow in the role. ### **What We Offer:** * The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment * A forward\-deployed model working in small, senior teams alongside FDE and FDX * A growing AI delivery practice where you help build the tooling and frameworks, not just use them * Remote\-friendly culture * Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance * Career growth;