Junior AI/ML Engineer (GenAI, AWS)

Role Summary

• 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.

How we hire

• Intro conversation. The role, your background and aspirations, tech questions.
• Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant
• HR Interview. Soft skills and expectations
• HM interview. Tech questions; a live engineering session is also possible

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.

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.

Benefits

• 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; 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