Factored

Forward Deployed ML/AI Engineer

Rol remoto de Machine Learning Engineering con fit claro de ubicación del candidato.

Publicado14 jul 2026

Países elegibles25 países aceptados

Señal de senioritySenior

Modelo de trabajoRemoto

Ubicaciones aceptadas para candidatos

ArgentinaBahamasBarbadosBeliceBoliviaBrasil+19 más

AWS Azure CI/CD Docker GCP Python SQL

Resumen del rol

Forward Deployed ML/AI Engineer

Requisitos y responsabilidades

Contenido del rol extraído en secciones para revisar más rápido.

Functional Responsibilities:

Cross-Functional Stakeholder Leadership: 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.
Business Problem Definition & Solution Architecture: 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.
End-to-End AI Application Development & Strategic Delivery: 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).
API, Pipeline Architecture & Infrastructure Design: 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.
Model Optimization, MLOps & Production Excellence: 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.
Knowledge Transfer & Capability Building: 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.

Qualifications:

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.

Nice to Have:

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.