AI Engineer

• Lead the design, development, and ownership of AI-powered internal platforms and systems.
• Own end-to-end delivery of complex projects, from problem discovery to production rollout.
• Design and implement AI-driven features, intelligent automation, and orchestration systems.
• Embed AI capabilities into customer-facing products, ensuring measured value delivery.
• Build full-stack systems spanning backend services, APIs, data layers, and frontend interfaces.
• Translate ambiguous problems into scalable, AI-enabled technical solutions.
• Collaborate closely with teams to identify high-impact opportunities and drive adoption.
• Mentor AI team members through code reviews, design feedback, and guidance.

AI Engineer II

• Diseñar e implementar aplicaciones basadas en IA Generativa
• Construir copilotos empresariales y agentes inteligentes
• Desarrollar soluciones de búsqueda semántica
• Implementar mecanismos de razonamiento y automatización

Ingeniero de Inteligencia Artificial – AI Engineer

• Design and execute advanced AI/ML models – including fine-tuned LLMs and RAG architectures – for integration into both internal tooling and external client-facing products.
• Lead experimentation efforts: clearly define hypotheses, design interpretable tests, analyze results rigorously, and communicate findings effectively.
• Serve as the technical SME for GenAI/LLM-powered tools, from foundational architecture to model optimization and deployment.
• Work with software and data engineering to productionize models and contribute to robust CI/CD pipelines with MLOps/LLMOps best practices.
• Stay current on emerging methods in AI – e.g., agentic workflows, memory-augmented systems, multimodal embeddings – and bring those ideas to life in real-world applications.
• Promote AI adoption internally by educating and empowering cross-functional partners while improving processes and workflows.
• Manage your time and projects effectively, ensuring clear progress updates and proactive communication with collaborators.
• Write production-grade Python code to support models, pipelines, and experimental frameworks.
• Work with the highest degree of ethics and empathy, recognizing that AI can be a potentially disruptive force for organizations and individuals.

Senior AI/ML Engineer – Technology

• Engage in pre-sales activities, including customer discovery sessions, workshops, and solution design
• Partner with Sales and Account teams to shape opportunities, define scope, and build compelling technical proposals
• Support RFP/RFI responses and present AI solutions to technical and executive stakeholders
• Assess customer AI, data, and infrastructure environments and propose scalable architectures
• Design and implement enterprise AI platforms based on Red Hat OpenShift AI and Red Hat OpenShift
• Lead MLOps adoption initiatives, including model lifecycle management, deployment automation, monitoring, and governance
• Architect solutions for model training, fine-tuning, serving, and inference at scale
• Support customers in deploying and operationalizing Generative AI and Large Language Model (LLM) workloads
• Provide technical leadership during delivery, ensuring alignment with architectural best practices
• Act as a trusted advisor to customers and partners, helping define AI and platform roadmaps
• Collaborate with Product Engineering, Consulting, and Support teams to deliver high-quality solutions
• Drive adoption of MLOps, DevOps, automation, and Infrastructure as Code (IaC) practices
• Contribute to internal knowledge sharing, reusable assets, and best practices

OpenShift AI Architect

• Diseñar, desarrollar y optimizar servicios backend escalables
• Construir y mejorar APIs y sistemas distribuidos
• Manejar LLMs, prompts y asegurar que el LLM se conecte a los microservicios de Kavak
• Desarrollar herramientas para que el LLM se comunique bien con el cliente
• Impulsar buenas prácticas dentro del equipo de GenAI
• Mejorar el flujo y la calidad de atención que se le brinda al cliente
• Experimentar con modelos y su integración en el backend
• Rediseñar y redefinir la estructura y el template de los bots

• Map current-state workflows end-to-end to diagnose root causes of operational friction, including handoff failures, governance gaps, and tooling mismatches.
• Redesign target-state operations for leaner workflows, clearer decision rights, and effective human-agent collaboration to accelerate throughput using AI.
• Design comprehensive operating models, including organizational structure, roles, governance, cross-functional interaction models, and value realization.
• Establish performance baselines and build measurement frameworks that track improvement continuously throughout the engagement.
• Identify value gaps in stalled implementations, diagnosing where technology deployment has outpaced business adoption to restore the path to ROI.
• Build working, AI-powered prototypes directly utilizing LLM APIs, platform-native features, and low-code/no-code environments to create functional solutions.
• Utilize working demos and prototypes as the primary tool for stakeholder alignment directly within the client's actual processes.
• Evaluate where AI practically enhances a workflow versus where it serves as a distraction, advising clients with realistic, grounded expectations.
• Support the rollout of new tools and workflows, ensuring adoption is rooted in process design and change management.
• Design enablement materials that ensure new technology implementations effectively connect to and support the intended operating model.
• Support complex engagement scoping and conduct diagnostic assessments for value capture, implementation readiness, and adoption strategy.
• Contribute reusable frameworks and tools that continuously improve practice qualification and scoping.

Forward Deployed Engineer – AI, Business Transformation

Machine Learning Engineer

• Diseñar, desarrollar e implementar soluciones de IA/ML para resolver problemas empresariales, contribuyendo a proyectos desde la prueba de concepto y piloto hasta el despliegue en producción.
• Construir y aplicar tanto modelos de aprendizaje automático tradicionales como técnicas modernas de IA, incluidos modelos de lenguaje grandes (LLMs), IA generativa, generación aumentada por recuperación (RAG) y sistemas de IA agenciales.
• Colaborar con ingenieros séniores, gerentes de producto, científicos de datos y partes interesadas empresariales para refinar el alcance del proyecto, el enfoque técnico, las métricas de éxito y los requisitos de despliegue.
• Contribuir a la construcción de sistemas de IA escalables y mantenibles con un enfoque en precisión, robustez, equidad y explicabilidad, adhiriéndose a los principios de IA responsable.
• Aplicar marcos de evaluación de modelos para evaluar el rendimiento, generalización, sesgo y resiliencia a casos extremos o entradas adversariales.
• Apoyar la transición de prototipos de IA a sistemas listos para producción colaborando con los equipos de plataforma e ingeniería en el despliegue, monitoreo y gestión del ciclo de vida.
• Contribuir a la documentación, gobernanza y procesos de cumplimiento para cumplir con las normas empresariales y regulatorias, especialmente en entornos regulados de atención médica.

AI/ML Engineer

• Liderar la operación tecnológica de Marketing mediante la construcción y gestión de plataformas, automatizaciones, agentes de IA y activos digitales.
• Administrar la operación y evolución continua de kushki.com.
• Liderar mejoras técnicas del sitio web orientadas a conversión, rendimiento y escalabilidad.
• Garantizar disponibilidad, seguridad y mantenimiento de las plataformas bajo su responsabilidad.
• Diseñar e implementar workflows automatizados para procesos de Marketing.
• Construir y evolucionar agentes de IA orientados a productividad y generación de demanda.
• Gestionar el data layer de Marketing y asegurar la consistencia de la información.
• Aplicar estándares de Quality Gates antes de despliegues en producción.

Marketing Intelligence Lead

• Analyze large datasets to surface trends, patterns, and actionable business insights.
• Build and maintain data models, transformations, and pipelines using SQL and dbt.
• Collaborate with stakeholders to define metrics, KPIs, and reporting requirements.
• Support data governance, ensuring data quality, integrity, and accessibility across the organization.
• Document processes, workflows, and analysis outcomes for cross-functional teams.
• Design, build, and evaluate supervised and unsupervised ML models (classification, regression, clustering, forecasting).
• Lead problem framing conversations with stakeholders to translate business questions into ML-ready problem statements.
• Conduct feature engineering, selection, and transformation to prepare data for model training.
• Validate and communicate model performance using appropriate evaluation metrics (AUC, RMSE, F1, precision/recall, etc.).
• Support lightweight model deployment and monitoring, flagging performance drift and recommending retraining triggers.
• Contribute to experiment design and A/B testing frameworks where applicable.

Senior ML Engineer

• Join Sitetracker's Operations team
• Build, test, and deploy AI agents and automations
• Manage Salesforce Flows and automation
• Work on afternoon shift
• Collaborate with the Director of Operations
• Deliver documented builds for quality review

Operations Associate – AI Developer

• Develop AI workflows, customize data pipelines, tune models, and engineer prompts to bring idea to prototype
• Work with subject matter experts to set up evaluation for AI workflows, ensuring rigor, quality and safety of content output
• Work with eng partners to integrate AI workflows into production
• Build and configure AI performance monitoring with proper reporting and alerts
• Optimize and maintain AI workflows for performance, reliability, and long-term scalability
• Conduct exploratory data analysis to answer key questions and test assumptions. Design experiments and build prototypes for proof-of-concept.
• Provide AI expert advice to product and eng partners in shaping product roadmap
• Partner closely with software eng, product, data eng, ML platform teams to scope and plan in execution
• Communicate timeline, milestones, findings w/ internal stakeholders
• Guide less experienced team members, sharing knowledge on LLM workflows and AI/ML model lifecycle
• Champion a culture of experimentation, continuous learning, and proactive problem-solving
• Stay current with emerging ML tools and technologies, integrating new techniques that elevate our product capabilities

Machine Learning Engineer, Applied AI

• Partner with AI/ML product teams to proactively identify the data needs for rapid experimentation and stable production deployment
• Ensure data availability by collaborating with Product teams
• Support Data Analysts, Data Scientists, MLOps engineers in developing optimized data models, pipelines and frameworks
• Build pipelines to ingest, clean, and transform structured, semi-structured, and unstructured data formats
• Conduct data profiling, validation, and anomaly detection
• Build Vector databases for semantic search and retrieval of unstructured content
• Collaborate with data scientists and ML engineers to design end-to-end generative AI solutions
• Build clean, error-free MVP ready data models

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