Forward Deployed ML/AI Engineer

Factored is a company that helps U.S. companies build and scale AI and ML teams with top talent from LATAM. As a Forward Deployed ML/AI Engineer, you will bridge the gap between AI research and production applications, managing the lifecycle of intelligent systems while collaborating with diverse stakeholders to deliver measurable business outcomes.

Responsibilities:

• Translate complex business requirements into technical AI specifications in collaboration with product, operations, and business teams
• Serve as the technical authority on AI/ML topics while remaining approachable to non-technical stakeholders
• Manage expectations, communicate risks transparently, and maintain stakeholder confidence through complex projects
• Lead alignment discussions when technical constraints conflict with business priorities
• Coordinate with customer teams to ensure end-to-end delivery
• Partner with customer leadership to clearly define business problems, success metrics, and constraints
• Structure ambiguous problems into clear technical requirements with explicit trade-off analysis
• Present multiple solution approaches with pros/cons framed in business terms (time, cost, risk, user impact)
• Validate that proposed technical solutions will actually solve the stated business problem
• Design, build, and maintain full-stack applications integrating classical ML models and Generative AI components
• Own delivery of AI applications from discovery through production deployment and ongoing optimization
• Anchor projects on shared customer OKRs and measurable business outcomes (not just technical deliverables)
• Architect scalable data pipelines, feature stores, and robust APIs to serve model predictions efficiently
• Design infrastructure for cost optimization, monitoring, and reliability
• Balance technical sophistication with operational simplicity and maintainability
• Oversee continuous integration, deployment, monitoring, and fine-tuning of models in production
• Establish monitoring and alerting to catch data drift, performance degradation, and cost overruns
• Maintain system reliability and ensure models deliver sustained business value post-deployment
• Mentor customer technical teams and upskill internal staff on ML/AI best practices
• Document architectural decisions, deployment procedures, and maintenance playbooks
• Leave the customer with increased technical capability and reduced dependency on external support

Requirements:

• 6+ years of Machine Learning, SWE Gen AI or DS experience (must have productionized models)
• 3+ years of implementation and customer-facing experience
• Proficiency with Classical ML & GenAI: In-depth knowledge of classical models (Scikit-Learn, XGBoost) and Generative AI architectures (LLMs, RAG pipelines, and Vector Databases)
• Full-Stack Development Capabilities: Strong engineering skills in backend development (Python, FastAPI/Flask) and ML frontend frameworks (Streamlit)
• MLOps & Production Deployment: Proven experience deploying, monitoring, and maintaining models in production (Docker, CI/CD pipelines)
• Business Problem Translation: Ability to translate business challenges into clear technical solutions, focusing on business outcomes and identifying root causes
• Executive Communication & Influence: Ability to explain technical concepts and trade-offs to executives in clear business terms, enabling informed decision-making
• Customer Relationship & Stakeholder Autonomy: Experience building trust with customers, managing stakeholders, and working independently in fast-paced, ambiguous environments
• Experience working with Databricks
• Experiment Tracking & Model Registry: Deep familiarity with tools like MLflow or Weights & Biases to track experiments, manage model packaging, and maintain an organized model registry
• Cloud Infrastructure: Experience setting up and managing AI/ML environments on cloud platforms (AWS, GCP, or Azure)
• Data Engineering Fundamentals: Background in building data pipelines, ETL processes, and working with SQL/NoSQL databases
• Model Optimization: Familiarity with reducing inference latency and managing compute costs (e.g., quantization, caching strategies)
• Agentic Workflows: Experience building autonomous AI agents or multi-agent orchestration frameworks