Senior AI/ML Engineer (GenAI, AWS)

About the Position

We are seeking a Senior AI/ML Engineer (GenAI, AWS) to join our global team. This role involves building production-ready AI solutions using GenAI and AWS technologies.

About Provectus

• Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn AI into measurable business outcomes through bespoke applications, managed services, and advisory engagements. Our offices are located in North America, LATAM, and EMEA, and we partner with clients worldwide.
• Our work focuses on two verticals: Financial Services & Insurance and Healthcare & Life Sciences. We deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens.
• Our team holds 100+ AWS certifications, is certified in Anthropic's Agentic SDLC program, and co-delivers AI Blueprint engagements.

Where this role sits

You will work in a small, senior pod alongside an FDE and an FDX:

• Forward Deployed Executives (FDX) manage the commercial relationship and business outcomes, working with client leadership to achieve KPIs.
• Forward Deployed Engineers (FDE) embed with clients, map workflows, identify business problems, design and build AI solutions, and transfer knowledge to client teams.
• Senior AI Engineer. You will help turn business problems into agentic systems that run in production, responsible for evaluation, observability, and guardrails.

Responsibilities

• Collaborate with FDE and FDX to build and ship production GenAI systems.
• Build and optimize RAG systems for production use cases.
• Develop evaluation harnesses before building features.
• Write production code across AI, backend services, and data pipelines.
• Integrate AI components into backend services and RESTful APIs.
• Deploy systems on AWS (GCP or Azure where required) using containerization, CI/CD, LLMOps, and AgentOps practices.
• Document and enable client engineers to inherit the system.
• Conduct model evaluation, optimize model performance, and mentor junior and mid-level AI engineers.

Requirements

Mindset

• Proactive and self-directed; excellent communication and problem-solving skills.
• Comfort with ambiguity and ownership.
• B2+ English proficiency, comfortable collaborating across distributed, multicultural teams.

Technical Depth

• 5+ years in software or ML engineering with production systems.
• Solid AI/ML foundations and understanding of model failure modes.
• Experience with LLM applications and agentic workflows.
• Agentic orchestration skills, including multi-step workflows and graph-based orchestration.
• Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks.
• Experience building and optimizing RAG systems in production.
• Strong engineering fundamentals with a full-stack mindset.
• Hands-on AWS experience with services like Bedrock, Lambda, ECS, S3, SQS, ECR, etc.
• Cloud-native delivery practices, including containers, IaC, and CI/CD.
• Experience building evaluation suites for non-deterministic systems.
• Model and agent monitoring, drift detection.
• Cost and latency discipline.
• Hands-on experience with the Claude ecosystem.
• MCP understanding and preference over REST integration.

Nice to Have

• Experience in financial services, insurance, or healthcare.
• Consulting or embedded customer-facing delivery experience.
• AWS and Claude Code Certifications.
• A2A interoperability understanding.
• CI/CD pipeline experience (GitHub Actions, GitLab CI).
• Practical experience with NLP, LLMs, and Recommendation engines.
• Experience with Go, TypeScript, or Rust.
• Experience with Apache Spark, Apache Airflow, Kafka.

We Offer

• Opportunity to shape AI adoption in leading enterprises.
• Work in a forward-deployed model with small, senior teams.
• Contribute to building tooling and frameworks in a growing AI delivery practice.
• Remote-friendly culture.
• Internal training programs with support for certifications and conference attendance.
• Career growth opportunities.
• Access to the latest AI tools and premium subscriptions.
• Long-term B2B collaboration.
• Private medical insurance or a budget for medical needs.
• Paid sick leave, vacation, and public holidays.
• Equipment and tech for comfortable, productive work.

How We Hire

1. Intro conversation about the role, your background, and aspirations.
2. Technical interview with live engineering sessions.
3. HR interview focusing on soft skills and expectations.
4. HM interview with technical questions and a live engine.

We may use AI tools to support parts of the hiring process, but final hiring decisions are made by humans. For more information on data processing, please contact us.

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