Senior AI/ML Engineer (GenAI, AWS)
Actively Hiring
Full-time Posted 24 days ago
Responsibilities
- check_circle Work in a pair with an FDE and an FDX.
- check_circle Build and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions).
- check_circle Build and optimize RAG systems for production use cases
- check_circle Build the evaluation harness before you build the feature.
- check_circle Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer.
- check_circle Integrate AI components into backend services and RESTful APIs
- check_circle 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
- check_circle 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
- check_circle Participate in technical discussions and architectural decisions
- check_circle Conduct model evaluation, improve failure modes you find, optimize model performance, efficiency, and reliability
- check_circle Mentor junior and mid-level AI engineers, conduct code reviews and share knowledge across the team through documentation, presentations, and workshops.
Basic qualifications
- 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.
- 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.
Preferred qualifications
- 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
Benefits
- check_circle The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment
- check_circle A forward-deployed model working in small, senior teams alongside FDE and FDX
- check_circle A growing AI delivery practice where you help build the tooling and frameworks, not just use them
- check_circle Remote-friendly culture
- check_circle Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance
- check_circle Career growth; we actively develop our engineers
- check_circle Access to the latest AI tools and premium subscriptions
- check_circle Long-term B2B collaboration
- check_circle Private medical insurance or a budget for your medical needs
- check_circle Paid sick leave, vacation, and public holidays
- check_circle Equipment and all the tech you need for comfortable, productive work
- check_circle Intro conversation. The role, your background and aspirations, tech questions.
- check_circle Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant
- check_circle HR Interview. Soft skills and expectations
- check_circle HM interview. Tech questions; a live engineering session is also possible
Tags & Focus Areas
Fulltime Remote Ai Ai Engineer Machine Learning Generative Ai
About Provectus
At Provectus, we are obsessed with technologies that impact the future of the whole of humanity. Just imagine: AI for curing eye diseases, ML for making factories safe for workers, preventing pandemic spreading, etc. It’s not about the future, it’s about the products we’ve already developed with ProvectusTeam.
Ready to Join the Team?
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