Job Summary
We are looking for an experienced AI Engineer Lead to drive the technical direction, development, deployment, and ongoing maintenance of enterprise-scale Machine Learning and Generative AI platforms.
The ideal candidate will have strong expertise in ML Engineering, MLOps, GenAI infrastructure, automated ML pipelines, model evaluation, and production operations.
This role requires a hands-on technical leader who can manage globally distributed engineering teams, work closely with client stakeholders, and ensure the reliability, scalability, security, and performance of production AI systems.
Required Qualifications
8+ years of experience in Software Engineering, ML Engineering, MLOps, or DevOps.
2+ years of experience in a technical leadership role.
Strong hands-on experience with Google Cloud Platform MLOps services, particularly Vertex AI and Kubeflow, or equivalent tools such as MLflow and Apache Airflow.
Strong programming skills in Python or Go.
Experience with large-scale data processing and distributed systems.
Hands-on experience with Kubernetes, Docker, and CI/CD tools such as Jenkins or GitLab CI.
Strong understanding of end-to-end ML pipelines, model training, deployment, evaluation, and continuous monitoring.
Experience with production monitoring, SLOs, incident management, and root-cause analysis.
Excellent technical leadership, communication, and stakeholder management skills.
Preferred Qualifications
Experience with LLMs and Agentic AI frameworks such as LangChain, AutoGen, LlamaIndex, or Agent Development Kit.
Knowledge of AI and ML evaluation metrics, including BLEURT, ROUGE, Precision, Recall, and AUC.
Experience handling sensitive data such as PII and PHI.
Knowledge of Grafana, Datadog, model drift detection, and A/B testing.
Experience with AI quality dashboards and production observability.
Previous experience in SRE, sustenance engineering, production support, or service-transition programs.
