VS
Lead Machine Learning Engineer
Veridic Solutions LLC
- 🌏 Worldwide
- Remote
- Staff / Principal
- 2 days ago
- CI/CD
- Load Testing
- Machine Learning
- Python
- TensorFlow
- PyTorch
- Hugging Face
- scikit-learn
- GCP
- Azure
- Docker
- Kubernetes
- Vertex AI
- MLflow
- IaC
- Databricks
- Linux
- REST API
- FastAPI
2 days ago
WHAT YOU WILL DELIVER
• ML system architecture: Define end-to-end architectures spanning data and feature access, training, model artifacts, serving, integration, monitoring, and operational controls for search and recommendation workloads.• Reusable platform patterns: Establish scalable patterns and shared components for training, batch and real-time inference, deployment, performance optimization, testing, and continuous delivery.
• Training and inference pipelines: Lead implementation of modular pipelines for data loading, preprocessing, training, artifact management, validation, and low-latency or high-throughput prediction.
• Search and recommendation enablement: Support retrieval, ranking, personalization, and recommendation models with appropriate serving strategies, APIs, feature pipelines, and system integrations.
• Performance engineering: Drive profiling, load testing, capacity planning, and optimization of compute, latency, throughput, memory, and resource utilization.
• Deployment and reliability: Guide containerization, orchestration, environment promotion, model versioning, rollback, health checks, and production-readiness practices.
• Observability and governance: Define standards for logging, metrics, alerts, data and model quality checks, traceability, auditability, and incident diagnosis.
• Cross-functional technical leadership: Align Data Science, platform, data, backend, and product partners on architecture, interfaces, dependencies, risks, and execution plans.
• Technical enablement: Lead design and code reviews, document reference implementations, and transfer knowledge so internal teams can maintain and extend delivered capabilities.
• Engagement outcomes: Deliver agreed software, infrastructure patterns, documentation, recommendations, and knowledge transfer within the contract scope and timeline.
REQUIRED QUALIFICATIONS
• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related quantitative field, or equivalent practical experience.• 6+ years of experience building and operating production machine learning systems, including technical leadership across complex ML engineering initiatives.
• Strong Python and distributed data processing skills, including experience with Spark and large-scale data or feature pipelines.
• Demonstrated ability to define reusable ML engineering patterns, influence architecture across teams, and lead delivery without relying on formal authority.
• Experience with deep learning or machine learning frameworks such as TensorFlow, PyTorch, Hugging Face, scikit-learn, or comparable technologies.
• Hands-on experience building training pipelines, batch or online inference systems, and model-serving APIs.
• Experience with cloud platforms such as GCP or Azure and production-oriented containerization and orchestration using Docker and Kubernetes.
• Experience with software engineering practices including package development, automated testing, CI/CD, version control, and code review.
• Understanding of APIs, distributed systems, model lifecycle management, monitoring, logging, and production reliability.
• Ability to diagnose and optimize performance across latency, throughput, concurrency, compute, and resource utilization.
• Ability to communicate architectural decisions, technical tradeoffs, risks, and delivery status to technical and non-technical partners.
PREFERRED QUALIFICATIONS
• Experience supporting search systems, recommender systems, learning-to-rank, retrieval, embeddings, personalization, or multi-stage ML pipelines.
• Experience with managed ML platforms and orchestration capabilities such as Vertex AI Pipelines, Azure Machine Learning, MLflow, or comparable tooling.
• Experience with feature stores or shared offline and online feature pipelines.
• Experience using infrastructure-as-code tooling and production observability platforms.
• Experience with Databricks, Linux, shell scripting, REST APIs, or FastAPI.
• Experience in retail, e-commerce, marketplaces, or another high-scale consumer domain.
• Experience establishing engineering standards, guiding technical reviews, and creating reference architectures across teams.
Lead Machine Learning Engineer · Veridic Solutions LLC