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Senior AI/ML Engineer (GenAI, AWS)
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Qventus —Capital Federal, Buenos Aires
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Syneos Health Commercial Solutions —Buenos Aires, Buenos Aires
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Vylor —Buenos Aires, Buenos Aires
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Senior AI/ML Engineer (GenAI, AWS)

Provectus -
Buenos Aires, Buenos Aires

Postulación rápida

Detalles del empleo

Tiempo completo

Descripción completa del empleo

About Provectus

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.

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.

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

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 engine

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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