Junior AI/ML Engineer

• 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 hel