Note: The job is a remote job and is open to candidates in USA. 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
Skills
- 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
Benefits
- Ownership through equity participation.
- Annual company retreat.
- Education bonus for continuous learning.
- Company-wide winter break.
- Paid time off.
- Optional in-person events and meetups.
- Tailored career roadmaps.
- High-performance culture.
Company Overview
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