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, Kafkа

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:

1. Intro conversation. The role, your background and aspirations, tech questions.

2. Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant 

3. HR Interview. Soft skills and expectations

4. HM interview. Tech questions; a live engineering session is also possible