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Medellín, Antioquia, Colombia
Remote only
You build it, you run it. End-to-end accountability for the AI platform in production: architecture, delivery, operations, cost, and quality, with on-call for what you ship. No hand-off to a separate ops or QA function.
Design, build, and operate agentic AI systems: LLM services, retrieval pipelines, multi-agent orchestration, agent execution runtimes, and human-in-the-loop capabilities.
Build and operate memory infrastructure for agent systems (storage, retrieval, promotion/demotion mechanics). Applied Science owns promotion criteria and scientific soundness.
Own LLMOps: the harnesses, pipelines, and infrastructure that keep non-deterministic systems reliable and correct in production, including dynamic model selection by task, cost, and latency.
Build automated eval infrastructure that continuously compares offline and online metrics and alerts on drift or regression. Applied Science owns the methodology and acceptance thresholds it checks against.
Evaluate, adopt, and integrate third-party LLMOps and eval platforms where they accelerate delivery over building in-house.
Build the technical controls, telemetry, and guardrails that enforce compliance frameworks (e.g., ISO 42001, AIUC-1) in the architecture itself.
Build and maintain the agent definition management APIs and the tool registry definitions reference, keeping definitions stable as tools and models version independently.
Lead the engineering build of proof-of-concepts. Applied Science defines the architecture bet and validation criteria being tested.
Create architecture, prototypes, and design proposals that deliver near-term business value while aligning with the future-state product and platform vision.
Influence technical direction through architecture reviews, RFCs, design discussions, and hands-on collaboration.
Partner with senior developers, architects, and tech leads to define and execute the product/platform architecture roadmap.
Balance delivery speed, quality, maintainability, and long-term platform health when making technical decisions.
Break down large initiatives into parallelizable chunks of work that deliver incremental business value.
Champion proposals through all phases of the SDLC, from design through production implementation.
Contribute to improving the core product build, CI/CD pipelines, and overall SDLC.
Troubleshoot and eliminate root causes of persistent production issues; drive reliability, observability, monitoring, and incident response.
Keep technical documentation current and create new artifacts as needed.
Facilitate design discussions within and across teams.
Represent the team in technical discussions with Tech Leads, Operations, Product/UX, Security, Domain SMEs, and external stakeholders.
Act as a trusted technical leader, helping teams succeed through collaboration, influence, and shared ownership.
Mentor other developers through design reviews, joint agentic-AI sessions, and feedback on skills, prompts, and other agent inputs; contribute to raising overall engineering maturity.
Share knowledge of new technologies and industry best practices.
Evaluate emerging technologies, frameworks, and cloud capabilities that could benefit the platform.
Use AI-assisted development tools — inline completion, conversational assistants, and agentic workflows — as a standard part of your daily workflow.
Critically evaluate and take ownership of all AI-generated output before it ships — verify correctness, security, and alignment with architecture and team standards.
Identify opportunities for agentic automation within your team, and lead the rollout of new AI-assisted workflows and tooling.
Mentor other developers on effective, responsible use of AI-assisted development tools.
Help establish team-level practices and guardrails for using AI tools safely and effectively.
Build and refine feedback loops that improve the team's collective use of AI tooling over time.
2+ years building and operating production AI systems, with a working grasp of the latency, cost, accuracy, and reliability trade-offs involved.
Experience with agent frameworks, agent memory systems, or orchestration of tool-using AI systems.
Production AWS experience, including Infrastructure as Code (CDK, CloudFormation, or Terraform).
Degree in Computer Science, Software Engineering, or equivalent practical experience; 8+ years of professional software development experience with demonstrated impact beyond a single team.
Proven experience designing and operating distributed, cloud-native, SaaS/multi-tenant platforms at scale, ideally on AWS.
Strong software fundamentals: OOP, design patterns, SOLID principles, and a track record of advocating for code quality.
Experience with agentic-AI software development practices.
Experience designing and consuming well-designed HTTP APIs (REST, GraphQL, or similar).
Experience mentoring developers and influencing architectural decisions across teams.
Strong written and verbal communication skills; comfortable operating in fast-moving environments with ambiguity and evolving requirements.
Strong initiative to improve processes, tools, methodologies, and product quality.
Experience implementing AI guardrails, governance controls, and safety mechanisms, and translating compliance frameworks (e.g., ISO 42001, AIUC-1, NIST AI RMF) into technical controls.
Experience evaluating and integrating third-party ML/LLMOps platforms, with sound buy vs. build judgment.
Experience with retrieval systems (RAG), embedding pipelines, or hybrid search (vector + keyword).
LLMOps experience: evaluation harnesses, automated offline/online metric comparison, drift and regression alerting.
Experience operating systems in regulated or compliance-heavy domains.
Familiarity with accounting, auditing, or financial workflows.
Reliability & Observability: New Relic, CloudWatch, Prometheus, OpenTelemetry or equivalent
Cloud & Infrastructure: AWS, EKS, Lambda, S3, DynamoDB, IAM, VPC
Containers & Orchestration: Docker, Kubernetes
Automation & Scripting: Python, Bash, TypeScript or equivalent
Incident & Operations: runbooks, alerting workflows, incident management tools, post-incident review practices
Tooling: GitHub, GitHub Actions, Jira, Confluence, Microsoft Teams, Slack