AI Engineer

• Review and evaluate AI-generated code across programming languages and software engineering scenarios
• Assess code for correctness, reliability, security, scalability, readability, and maintainability
• Identify bugs, logical errors, incomplete implementations, edge-case failures, and architectural weaknesses
• Analyze unfamiliar codebases and understand the impact of proposed changes
• Review bug fixes, feature implementations, refactoring, API integrations, configuration changes, and database operations
• Compare alternative implementations and determine whether they satisfy technical requirements
• Rewrite or improve code to create high-quality reference solutions
• Provide clear technical explanations of identified issues and recommended improvements
• Develop evaluation criteria, technical annotations, and rubrics for code-quality assessment
• Collaborate with researchers and engineering teams to design coding benchmarks and improve LLM evaluation methodologies

Senior Software Engineer – AI Code Evaluation

• Design module content for the FinTech Engineering Specialization at Jala University
• Develop labs
• Create a reference implementation
• Build an evaluation harness
• Prepare an instructor guide

Academic Content Developer – AI, Fraud Detection, Financial Systems

• Own end-to-end program delivery across scope, timelines, quality, throughput, contributor performance, and cost.
• Design and manage workflows for coding datasets, agentic trajectories, RL environments, benchmarks, and rubric-based evaluations.
• Identify operational bottlenecks and improve workflows through better instructions, sequencing, incentives, review systems, and capacity planning.
• Define contributor requirements and partner with talent teams to source, assess, onboard, train, and ramp distributed software engineers.
• Build team-lead and reviewer structures for programs involving 100–1,000+ contributors.
• Own quality-control systems and analyze datasets to identify trends, systematic errors, and root causes.
• Act as a primary customer contact for AI labs, communicating progress, risks, quality trends, and recovery plans.
• Translate research objectives into practical task specifications and challenge requirements when they may not produce the intended evaluation signal.
• Use Python, SQL, or similar tools to automate quality sampling, defect analysis, throughput reporting, and operational reviews.
• Convert successful workflows into reusable playbooks, quality controls, evaluation assets, and contributor-management systems.
• Share operational learnings and mentor other program leads.

Technical Program Operations Lead – AI Engineering

• Build and maintain Unity (C#) field applications for sketching, configuring, and publishing pipe, duct, and other MEP work from jobsites
• Write production Unity code covering scene and prefab architecture, component lifecycle, input handling, UI, performance, and editor/runtime separation
• Own client-to-cloud integration, login, publishing, data synchronization, fidelity, and error handling between the Unity client and Stratus services
• Build and maintain automated Unity tests using NUnit, PlayMode integration tests, and EditMode unit tests
• Ship customer-facing enhancements across backend services, APIs, data, and web front end
• Design and build services and APIs connecting the web platform and cloud data
• Investigate and fix customer-reported bugs across client, service, and data layers
• Implement unit, integration, and end-to-end automated testing
• Contribute to monolith decomposition and modernization
• Use AI-assisted development tools and validate AI-generated changes with tests and engineering judgment
• Collaborate with product managers, designers, and customer-facing teams to define, design, scope, and ship features
• Coordinate deployments and incident response
• Advocate for incremental delivery
• Share knowledge through code reviews and pragmatic best practices

Unity Developer – AI Forward

• Design and build AI-native systems end-to-end, from architecture through production
• Write production code in Node.js and Python, and build interfaces in React and TypeScript
• Own deployment on Azure, including CI/CD pipelines that support fast iteration
• Build agentic systems using LLM tool-use, MCP, and agent runtimes
• Work directly with the founder to define problems and shape the approach
• Document your work so the team can build on it

Full-Stack AI Architect

• Design, build, and deploy production-grade data pipelines, analytics engines, and Generative AI/LLM applications using Databricks, Spark, Python/PySpark, and modern cloud ecosystems
• Serve as the primary technical leader on strategic enterprise client engagements
• Partner with C-level executives, VP/Directors of Data, and engineering teams to translate business challenges into technical solutions
• Lead complex legacy platform migrations onto the Databricks Lakehouse architecture
• Guide enterprise customers in adopting GenAI solutions, RAG architectures, model fine-tuning, and MLflow/Databricks Mosaic AI integrations
• Identify high-value technical use cases across client business units, expand Databricks platform usage, and drive measurable client ROI
• Mentor junior Solution Engineers, Architects, and Consultants
• Contribute to internal RevStar code libraries, reusable accelerators, and technical delivery best practices

Forward Deployed Engineer, AI/ML

• Create realistic, challenging coding tasks involving feature implementation and software bug fixes.
• Design tasks that reflect real-world backend engineering scenarios.
• Develop deterministic verifiers that accept reasonable valid solutions and reject incorrect outputs.
• Work with backend technologies such as Java, Node.js, Go, Rust, TypeScript, Python, C++, or similar languages.
• Evaluate code for correctness, reliability, maintainability, and performance.
• Incorporate automated testing and clear validation criteria into coding tasks.
• Identify and account for edge cases, performance bottlenecks, and potential implementation errors.
• Apply secure coding principles and cybersecurity considerations where relevant.
• Refine tasks and verification logic based on project requirements and quality standards.

Backend Software Engineer – AI Training Projects

• Design, build, and evolve scalable backend services and APIs using Node.js, TypeScript, and Express.js
• Own technical solutions end-to-end from design and implementation through deployment, observability, and production support
• Contribute to backend architecture and architectural and technical design decisions
• Identify, prioritize, and address technical debt
• Apply software design principles and patterns based on context and trade-offs
• Design application boundaries and separation of concerns across services, business logic, and persistence layers
• Design and optimize PostgreSQL schemas, queries, indexes, migrations, and transactional workflows
• Build reliable financial workflows covering idempotency, concurrency, consistency, retries, failure handling, and reconciliation
• Design and maintain REST APIs and integrations with internal systems and external providers
• Design asynchronous and event-driven workflows
• Build reliable, scalable, resilient, secure, and performant systems
• Work with AWS infrastructure and cloud services in production systems
• Improve production visibility through logging, metrics, tracing, monitoring, and alerting
• Investigate production incidents, perform root-cause analysis, and implement sustainable solutions
• Maintain engineering quality through automated testing, code reviews, documentation, and continuous refactoring
• Use AI-assisted engineering tools for development, testing, debugging, code review, and productivity
• Challenge technical decisions and improve engineering standards
• Share knowledge and strengthen the engineering team’s technical capabilities
• Collaborate with Product, QA, Data, Platform, and engineering teams to translate business requirements into technical solutions

Backend Developer, AI

• Design and build production-grade services, APIs, SDKs, internal tools, and reusable platform capabilities
• Develop systems combining AI agents, tools, deterministic workflows, APIs, events, and human approval steps
• Implement agent runtimes, model invocation, reusable skills, backend services, and automated actions
• Build orchestration patterns involving routing, sequencing, parallel execution, delegation, retries, checkpoints, scheduling, memory, and state management
• Integrate enterprise context through MCP, RAG, repositories, tickets, documents, databases, search systems, and APIs
• Connect the Harness with GitHub, Azure DevOps, Jira, Slack, cloud services, SaaS products, and internal applications
• Implement identity, permissions, policies, approval gates, guardrails, access controls, audit trails, and operational kill switches
• Build observability for agent activity, distributed traces, logs, metrics, token consumption, model and tool calls, failures, latency, and cost
• Create automated evaluations, benchmark datasets, regression suites, quality thresholds, hallucination checks, and model or prompt comparisons
• Improve developer experience through APIs, SDKs, CLIs, templates, configuration systems, documentation, and reusable reference implementations
• Build lightweight user interfaces, administrative tools, approval experiences, or operational dashboards when required
• Deploy and operate solutions across AWS, Azure, or Google Cloud using containers, serverless services, CI/CD, infrastructure as code, IAM, networking, and secrets management
• Conduct technical discovery in unfamiliar client environments and translate business and technical needs into scalable implementation patterns
• Move from discovery and experimentation to working integrations and production-ready solutions
• Communicate architecture decisions, risks, and trade-offs to client engineers, architects, product teams, and technical leadership

Senior Forward Deployed Engineer, AI Platform

• Design, develop, and deploy enterprise AI agents and multi-agent solutions using Google's AI ecosystem
• Leverage enterprise data, semantic models, and cloud services to deliver scalable, secure, and Responsible AI solutions
• Develop and deploy multi-agent solutions using Gemini and Agent Builder
• Design and implement RAG solutions leveraging EDW data and Looker semantic models
• Integrate AI agents with enterprise APIs, databases, and Google Cloud Platform services
• Optimize agent performance, orchestration, and user experience for business-critical use cases
• Implement Responsible AI practices, including governance, security, monitoring, and compliance controls

AI Engineer jobs in Colombia