Junior AI/ML Engineer (GenAI, AWS) @ Provectus — Vaga Remota

Requirements

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.

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.

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, Kafka.
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.

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.

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.

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.