Senior AI/ML Engineer (GenAI, AWS)
-
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 small, senior pod alongside an FDE and an FDX,
-
Forward Deployed Executives (FDX) own the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs.
-
Forward Deployed Engineers (FDE) embed with a client, map the client workflow, identify the business problem underneath it, design and build a working AI solution, present to the client, and transfer the knowledge to the client's team. Owns technical direction of the whole solution.
-
Senior AI Engineer. When an FDE comes back from the client with the business problem, you will help turn that into an agentic system that runs in production and will be responsible for evaluation, observability, and guardrails. You'll have real ownership of components and of the technical decisions inside them.
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, insuranc