Senior AI/ML Engineer (GenAI, AWS)

Requirements:

Mindset

• Proactive and self-directed; you push for clarity rather than waiting for a ticket
• Excellent communication and problem-solving skills
• Comfort with ambiguity and ownership.
• B2+ English, comfortable collaborating across distributed, multicultural teams.

Technical depth

• 5+ years in software or ML engineering, with production systems you were accountable for.
• Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes.
• Shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks.
• Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure.
• Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks.
• Experience building and optimizing RAG systems in production.
• Strong engineering fundamentals. Full-stack mindset, comfortable across AI, backend development, and cloud infrastructure. Python and/or TypeScript proficiency; depth matters more than stack. Dropped into an unfamiliar codebase, you're productive.
• Hands-on AWS in production: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus.
• Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines.
• You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release.
• Model and agent monitoring, drift detection.
• Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs.
• Hands-on production 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 — is a strong plus.
• MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus.

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.
• A2A: you can explain agent-to-agent interoperability.
• 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.

Responsibilities:

• Work in a pair with an FDE and an FDX.
• Build and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions).
• Build and optimize RAG systems for production use cases.
• Build the evaluation harness before you build the feature.
• Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer.
• Integrate AI components into backend services and RESTful APIs.
• Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD. Implement LLMOps and AgentOps practices: agent tracing, prompt and version management, cost and latency monitoring, regression testing, drift detection.
• Start from the blueprint, contribute to enablement and handover: clear documentation, runbooks, and pairing with the client engineers who will inherit the system. Feed reusable components and lessons back into the Provectus Blueprints.
• Participate in technical discussions and architectural decisions.
• Conduct model evaluation, improve failure modes you find, optimize model performance, efficiency, and reliability.
• Mentor junior and mid-level AI engineers, conduct code reviews and share knowledge across the team through documentation, presentations, and workshops.

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; 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.

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.