Machine Learning Engineer

Introduction

Employees in this job function are responsible for designing, building, deploying, and scaling complex self-running ML solutions in areas like computer vision, perception, localization, etc. They also automate and optimize the end-to-end ML model lifecycle using their expertise in experimental methodologies, statistics, and coding for tool building and analysis.

Required Skills & Qualifications

• GCP, Big Query, Python, Java, Cloud Infrastructure, Artificial Intelligence & Expert Systems
• Engineer 2 Experience: Practitioner in one coding language or framework, with 7 years in IT and 3 years in development
• 2 Years in AI and Graph Engineering
• Strong software engineering skills in Java and Python, with production-grade testing, CI/CD, and code quality practices
• Hands-on experience deploying data/AI systems to Production on a GCP-native stack: Vertex AI, BigQuery, Dataflow / Apache Beam, Pub/Sub, Cloud Run / GKE, Cloud Storage, and Cloud Build / Artifact Registry
• Experience with graph data modeling and querying — property graphs and GQL / graph query patterns
• Hands-on experience with Vertex AI (Agents, model serving, embeddings) and evaluation of agent answer quality
• Experience building LLM/agent systems: tool-use, RAG/grounding, and integrating models via APIs
• Observability expertise: Cloud Monitoring/Logging, OpenTelemetry, SLOs, dashboards, and alerting for data pipelines and services
• Infrastructure as Code (Terraform) and secure-by-default engineering (IAM, least privilege, secrets management)
• Ability to work directly with data producers to model and validate real-world industrial/enterprise data
• Prior work experience at client or in client's Industry

Applicants must be able to work directly for Artech on W2

Preferred Skills & Qualifications

• Familiarity with Dataplex / Data Catalog for governance, lineage, and business glossaries
• Streaming/CDC and event-driven architectures; append-only/event-sourced data modeling
• Design and build user-facing applications and dashboards that surface Knowledge Graph data to end users
• Domain exposure to PLM / product development, manufacturing execution, quality, or supply-chain systems and their data
• Data quality frameworks, schema evolution, and blue-green/zero-downtime data deployments

Day-to-Day Responsibilities

• Collaborate with business and technology stakeholders to understand current and future ML requirements
• Design and develop innovative ML models and software algorithms to solve complex business problems in both structured and unstructured environments
• Design, build, maintain, and optimize scalable ML pipelines, architecture, and infrastructure
• Use machine language and statistical modeling techniques to develop and evaluate algorithms to improve product/system performance, quality, data management, and accuracy
• Adapt machine learning to areas such as virtual reality, augmented reality, object detection, tracking, classification, terrain mapping, and others
• Train and re-train ML models and systems as required
• Deploy ML models and algorithms into production and run simulations for algorithm development and test various scenarios
• Automate model deployment, training, and re-training, leveraging principles of agile methodology, CI/CD/CT, and MLOps
• Enable model management for model versioning and traceability to ensure modularity and symmetry across environments and models for ML systems

For immediate consideration please click APPLY to begin the screening process with Alex.