AI Engineer

Sourced from matchmoth.com

Lead the technical direction and roadmap for ML sustenance across a multi-service portfolio, mentoring a globally distributed team. Design and sustain automated continuous training, data ingestion, and feature engineering pipelines, plus agentic evaluation infrastructure and quality dashboards. Own deployment, serving, reliability, observability, privacy/security, and cost optimization for production ML and GenAI systems.

• Set technical direction and roadmap for ML sustenance across a multi-service portfolio
• Lead and mentor a globally distributed follow-the-sun engineering team
• Design and sustain automated continuous training, data ingestion, and feature engineering pipelines
• Build agentic evaluation infrastructure, golden datasets, and quality dashboards
• Build and maintain CI/CD workflows for safe deployment and rollback of containerized ML models
• Set up observability (metrics, logs, traces) and participate in on-call incident triage
• Enforce data isolation, IAM, RBAC, and compliance-aligned retention policies
• Develop AI-assisted operational automation and optimize compute and token costs

• 8+ years in software engineering, ML engineering, or MLOps/DevOps across the ML lifecycle
• 2+ years in a technical lead role
• Experience with GCP MLOps services (Vertex AI, Kubeflow) or equivalent open-source ecosystems (Airflow, MLflow)
• Proficiency in Python or Go and large-scale data processing/distributed systems
• Experience with Kubernetes, Docker, and CI/CD pipelines (Jenkins, GitLab CI)
• Production operations experience: monitoring, alerting, SLOs, on-call triage, root-cause analysis
• Proven ability to design and implement end-to-end ML pipelines
• Strong cross-timezone leadership and stakeholder communication

• Familiarity with LLMs and agentic AI frameworks (LangChain, AutoGen, LlamaIndex)
• Knowledge of ML evaluation metrics (BLEURT, ROUGE, Precision/Recall, AUC, Trajectory Exact Match)
• Experience managing sensitive data (PII/PHI) with strict access control and encryption
• Experience with drift detection, A/B testing, and quality dashboards (Grafana, Datadog)
• Experience in sustenance, support-engineering, or SRE organizations