AI Engineer Lead

• 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 Leadwith 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.