• Architect GitLab's AI-native learning ecosystem, including adaptive learning paths, coaching agents, bots, intelligent recommendations, and automated content workflows. • Lead GitLab's company-wide AI fluency and enablement strategy in partnership with the Enterprise AI team, from baseline literacy through builder capability. • Embed AI fluency into onboarding, leadership development, and role-specific learning pathways. • Own the multi-year learning platform strategy and roadmap, including platform evaluations, migrations, integrations, and capability expansions. • Drive operational excellence across the Talent Management & Development team by managing the product roadmap, release schedule, intake processes, documentation, automations, and cross-functional coordination. • Partner with People Technology as the technical lead for Talent Development, translating learner and business needs into architecture briefs and co-building agents, workflows, and platform integrations. • Partner with People Analytics to define measurement infrastructure and dashboards for learning engagement, AI adoption, behavior change, capability growth, and program return on investment. • Lead global compliance training and vendor management, including audit readiness, negotiations, renewals, quarterly business reviews, budgets, adoption targets, and investment cases.

Role Description We are building Joblogic’s AI Agent Platform — a multi-tenant system for designing, versioning, evaluating, and running AI agents that work across email, voice, SMS, WhatsApp, and CRM channels on behalf of our customers. The platform is built on a LangGraph runtime with a full agent lifecycle: a prompt-driven agent builder, a tool and knowledge-base registry, human-in-the-loop review, an agent memory subsystem, and a rigorous evaluation harness backed by LangSmith and PromptFoo. We are looking for a mid-level AI/ML Engineer to help us design, build, and continuously improve these agents. In this role you will own agent behaviour end to end — crafting and engineering prompts, wiring up tools and retrieval, and, most importantly, building the evaluations and datasets that prove an agent is doing the right thing before and after it ships. You will also bring applied machine learning depth: working with our execution and conversation data, building models and analyses that make agents smarter, and using platforms such as Databricks or AWS SageMaker to train, track, and serve them. You will work closely with product, backend, and platform engineers, and your work will directly shape how tens of thousands of field-service businesses experience intelligent automation. Qualifications - Strong Python engineering skills, with experience building production services and clean, well-tested code. - Hands-on experience building LLM-powered applications or AI agents — for example with LangChain / LangGraph, the OpenAI / Anthropic APIs, or comparable frameworks. - Demonstrable prompt engineering skill: designing, iterating on, and versioning prompts. - Practical experience with LLM evaluation: building datasets, defining rubrics/metrics, running evals, and interpreting results. - A solid machine learning foundation: framing problems, feature engineering, training and evaluating models. - Hands-on experience with a modern ML platform — Databricks or AWS SageMaker (or equivalent). - Strong data analysis skills using Pandas, NumPy, and SQL. - Understanding of retrieval-augmented generation (RAG): embeddings, vector/hybrid search, chunking, and grounding/faithfulness. - Experience working with APIs, JSON schemas, and integrating third-party services from Python. - Familiarity with both SQL and NoSQL databases. - Committed to continuous learning, proactive problem-solving, and timely issue identification. - Strong communicator, experienced in collaborating with cross-functional teams using tools such as Jira and Slack. - Creative and innovative thinker, consistently contributing fresh ideas and solutions. Requirements - Build and improve AI agents — design agent behaviour on our LangGraph runtime, configure abilities, attach tools and knowledge bases, and take agents from draft through evaluation to production deployment. - Prompt engineering — author, version, and systematically improve system prompts. - Design and run evaluations — build datasets, author heuristic and LLM-judge rubrics, run offline evaluations and online scoring against live executions. - Retrieval & knowledge (RAG) — build and tune retrieval-augmented generation over our knowledge bases. - Tools & integrations — develop and integrate the tools agents call. - Data analysis — analyse execution traces, conversation transcripts, and outcome data. - Applied ML — build, train, evaluate, and deploy machine learning models. - MLOps — use platforms such as Databricks or AWS SageMaker for feature engineering, experiment tracking, model training, and serving. - Observability & quality — use tracing and monitoring to debug agent runs in production. - Collaborate & ship — work in a cross-functional team using tools such as Jira and Slack, write clear documentation, and ship iteratively. Benefits - Professional Working environment - Market Competitive Salary - Life Insurance & Medical Insurance (Including Family) - OPD - Provident Fund - Gym Facility - Maximum 45 Weekly Hours (Monday–Friday) - Remote Working (During Pandemic Situation) - Company trip - 29 Annual Leaves - 8 Sick & uncapped Compassionate Leaves (As per Company Policy)

Role Description At arrivia, we specialize in making brands better through the power of travel, combining decades of experience with a constant drive for technology innovation. This specific role is a fully remote opportunity based within Mexico. This role sits at the intersection of machine learning, cybersecurity, and full-stack engineering. You won't just be monitoring dashboards; you’ll be architecting intelligent defenses that keep our members and data safe from evolving digital threats by building high-scale, real-time security systems. As a core member of our IT Operations team, reporting to the Security Ops Lead, you will: - Design & Architect: Implement and scale AI/ML models to proactively detect, prevent, and respond to security threats such as identity risk, fraud, malware, and insider anomalies. - System Reliability: Build and maintain robust data ingestion, feature engineering, and real-time streaming pipelines (e.g., utilizing SQL/NoSQL databases and messaging frameworks like Kafka) to process high volumes of security events. - Deploy Microservices: Integrate AI security models directly into production workflows by developing high-performance, low-latency APIs and microservices (using frameworks like FastAPI, Flask, or Django). - Adversarial Research: Analyze adversarial attacks, threat modeling, and secure model design to adapt our defenses under evolving threat scenarios. - Automated Response: Enhance automated incident response, accelerating remediation workflows to significantly reduce manual threat investigation cycles. - Collaborative Impact: Partner with DevSecOps and Platform teams to build MLOps pipelines and embed AI-driven protections directly into containerized environments (Docker/Kubernetes). Qualifications - Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field (Master's preferred). - 5+ years of software engineering experience, with a heavy focus on backend architecture, API design, and distributed systems. - 3+ years of experience in cybersecurity, fraud detection, trust & safety, or abuse prevention. - 2+ years of hands-on experience building and deploying machine learning models in a production environment. - High proficiency in English with strong collaboration and communication skills. - Must be based in Mexico and available to work remotely. Requirements - Skilled in Modern Tech: Solid foundation in Python, SQL, and ML frameworks like PyTorch, TensorFlow, or scikit-learn. - Experienced with Distributed Systems: Comfortable working with large-scale data systems, stream processing, and containerized deployments using AWS, Docker, and Kubernetes. - Security Focused: Strong background in cybersecurity, fraud detection, trust & safety, or threat intelligence, alongside a deep understanding of secure system design and OWASP principles. - API & Backend Specialist: Proven experience designing robust, scalable microservices capable of handling strict latency and reliability constraints. - Analytical Problem-Solver: Ability to effectively balance the trade-offs between rigorous security, high system performance, and a positive user experience. - Growth Mindset: Enjoy staying up-to-date with secure AI frameworks (such as ISO/IEC 42001 or OWASP) and exploring advanced methods like time-series anomaly detection and graph analysis. Benefits - Collaborative, inclusive environment where your work directly impacts how millions of people experience the world. - Opportunity to apply your AI and security expertise to a global mission from the comfort of your home office in Mexico.

• Own internal tools end to end - from understanding the operational problem through design, implementation, deployment, and iteration. • Build across the stack as the product requires: Python/Go services on Cloud Run, TypeScript web apps and internal admin tools, event-driven integrations on Pub/Sub, and cloud infrastructure on GCP. • Integrate with third-party SaaS platforms and cloud APIs - handling REST, webhooks, OAuth, and event-driven patterns, including failures, retries, and schema drift. • Build and maintain workflow automation and business process orchestration as named, observable flows rather than logic buried in vendor scripts. • Use AI tooling effectively across the development lifecycle - spec, code, and review - directing Claude as a coding collaborator while owning the output. • Review AI-generated code before it ships for correctness, security, and type safety. • Operate what you ship: monitoring, reliability, cost, and failure modes in production. • Contribute to shared engineering standards, playbooks, and internal tooling.