- 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.

Core Responsibilities- 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.AI-Augmented Development Expectations:- 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.

What You Will Bring:- 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, Cloud

Formation, 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.

Nice to Have- 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, Cloud

Watch, Prometheus, Open

Telemetry or equivalent- Automation & Scripting: Python, Bash, Type

Script or equivalent- Incident & Operations: runbooks, alerting workflows, incident management tools, post-incident review practices- Tooling: Git

Hub, Git

Hub Actions, Jira, Confluence, Microsoft Teams, Slack- ¨Contrato a termino Indefinido¨ with all the legal benefits- Life insurance and funeral assistance- Home office stipend- Competitive compensation — above the market average- 100% remote work environment and an excellent work-life balance- 5 Personal Time Off days per year- Sick Leave Top up to total 100% of salary paid by the employer from Day 3 to 90.- Recognition Award, additional paid time off in recognition of the corresponding year of service- Upgrade vacation starting at 5 years of service- Opportunity to work for a growing general SaaS leader company- A culture that promotes independence, innovation, trust, and accountability- Open space to be creative, innovative and strategize for the future- Mentorship by highly experienced professional- Budget for training, we want you to grow- AI-first environment: Be part of an AI-first engineering organization that embraces modern tools, automation, and AI-driven ways of working.#J-18808-Ljbffr

📌 Artificial Intelligence Software Developer (Colombia)
🏢 CaseWare
📍 Colombia