1 month ago
Bogotá, Colombia +2 moreSenior
Responsibilities
- Perform the customer’s operational job for two to four weeks to understand its constraints from inside the process.
- Redesign business functions from first principles with operators and Forward Deployed Executives.
- Build and ship production GenAI systems, including LLM applications, agentic workflows, retrieval and structured-extraction pipelines, backend services, data pipelines, and AI layers.
- Create evaluation harnesses and define measurable success criteria before building features.
- Deploy maintainable, observable, containerized systems on AWS or other clouds when required.
- Drive customer adoption, contribute to business KPIs, and feed field learnings back into reusable industry blueprints.
- Shape engagement commitments and communicate credibly with customer engineers, operators, and executives.
Requirements
- At least 8 years of software-building experience, including substantial accountability for production code.
- Current hands-on production engineering experience and willingness to spend weeks performing an operator’s job before coding.
- Demonstrated ability to learn unfamiliar business domains quickly and work autonomously through ambiguity.
- Production experience shipping GenAI or LLM systems and owning evaluation suites for non-deterministic systems.
- Proficiency in Python and/or TypeScript, with strong engineering fundamentals across unfamiliar codebases or languages.
- Cloud-native delivery experience on AWS, including containers, Kubernetes or ECS, infrastructure as code, CI/CD, and operational ownership.
- Ability to work credibly with business-unit leaders, CTOs, customer engineers, operators, and executives.
- Solid AI/ML foundations and the ability to reason about model failure modes.
- Fluent written and spoken English.
- Preferred qualifications include founder, CTO, or engineering leadership experience; depth in financial services, insurance, healthcare, or asset management; consulting or embedded delivery experience; data platform expertise; MLOps or classical ML experience; fine-tuning, distillation, or inference optimization experience; graph database experience; infrastructure-as-code depth; and open-source contributions or public applied-AI writing.
Benefits
- Remote-friendly culture.
- B2B contract model.
- Performance-based bonuses and strong earning potential.
- PTO policy and paid local public holidays.
- Medical insurance coverage.
- Generous educational-opportunity budget.
- Opportunity to work on frontier AI delivery, enterprise adoption, and reusable tooling and frameworks.
Tech Stack
Categories
Forward Deployed
