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AI Engineer Lead

GeekSoft Consulting

  • San Jose, CA
  • 1 day ago

    Highlights

    Lead globally distributed engineering teams, drive end-to-end ML lifecycle management, and ensure scalable, secure, and reliable AI model deployment, evaluation, and monitoring. Strong expertise in GCP MLOps services, particularly Vertex AI and Kubeflow, or equivalent tools such as MLflow and Apache Airflow.

    Numbers & Facts

    LocationSan Jose, CA

    Description

    • Help design, build and continuously improve the clients online platform.
    • Research, suggest and implement new technology solutions following best practices/standards.
    • Take responsibility for the resiliency and availability of different products.
    • Be a productive member of the team.



    Requirements

    • 8+ years of experience in Software Engineering, ML Engineering, MLOps, or DevOps, including 2+ years in a technical leadership role.
    • Experienced ML/MLOps Technical Lead with strong expertise in machine learning operations, GenAI, Agentic AI, and production ML infrastructure.
    • Lead globally distributed engineering teams, drive end-to-end ML lifecycle management, and ensure scalable, secure, and reliable AI model deployment, evaluation, and monitoring.
    • Define technical strategy and roadmap for ML sustenance across multiple services.
    • Act as the primary onsite technical contact and lead globally distributed engineering teams.
    • Drive architecture reviews, define Service-Level Objectives (SLOs), and coordinate with client teams for AI agent onboarding.
    • Standardize model evaluation processes and mentor engineering teams.
    • Design, develop, and maintain automated Continuous Training (CT), data ingestion, and feature engineering pipelines.
    • Work with MLOps frameworks and tools such as Kubeflow, MLflow, and Apache Airflow.
    • Integrate datasets from distributed SQL systems and messaging platforms such as Apache Kafka.
    • Implement model, prompt, and evaluation configuration versioning, experiment tracking, and hyperparameter tuning.
    • Build isolated agent execution environments using Kubernetes and Docker.
    • Develop evaluation pipelines, synthetic datasets, golden datasets, and simulated execution traces.
    • Establish quality dashboards and automated monitoring using metrics such as Accuracy, AUC, ROUGE, and Trajectory Exact Match.
    • Implement alerting mechanisms to detect model drift, hallucinations, and quality degradation.
    • Evaluate and monitor automated assessment frameworks, including LLM-as-a-Judge.
    • Build and maintain CI/CD pipelines for automated ML model deployment and rollback.
    • Implement monitoring across metrics, logs, and traces to ensure production reliability and low-latency model serving.
    • Handle production incidents, troubleshooting, traffic rollouts, and root-cause analysis.
    • Optimize system performance, resolve capacity bottlenecks, and improve MTTR.
    • Implement data isolation mechanisms for sensitive and multi-tenant environments.
    • Enforce IAM, RBAC, encryption, and Zero Trust security principles.
    • Ensure compliance with data retention, access control, and deletion policies.
    • Develop AI-driven operational automation, including monitoring agents, automated incident triage, and documentation synchronization.
    • Optimize infrastructure, compute, and token costs across model serving and evaluation workloads.
    • Strong expertise in GCP MLOps services, particularly Vertex AI and Kubeflow, or equivalent tools such as MLflow and Apache Airflow.
    • Proficiency in Python or Go, distributed systems, and large-scale data processing.
    • Hands-on experience with Kubernetes, Docker, containerized deployments, and CI/CD tools such as Jenkins or GitLab CI.
    • Strong understanding of end-to-end ML pipelines covering data ingestion, model training, evaluation, deployment, and continuous monitoring.
    • Experience in production operations, SLO definition, monitoring, alerting, incident management, and root-cause analysis.
    • Proven experience leading distributed teams and managing technical and business stakeholders.
    • Knowledge of LLMs and Agentic AI frameworks such as LangChain, AutoGen, LlamaIndex, or Google Agent Development Kit (ADK).
    • Experience with ML and GenAI evaluation metrics, including BLEURT, ROUGE, Precision, Recall, AUC, and Trajectory Exact Match.
    • Experience handling sensitive data such as PII/PHI with strong encryption and access controls.
    • Knowledge of model monitoring, data/concept drift detection, A/B testing, and quality dashboards using Grafana or Datadog.
    • Experience with production support, ML sustenance, SRE, service transition, and knowledge-transfer programs.



    Benefits

    • A challenging, innovating environment.
    • Opportunities for learning where needed.


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