AI/ML Architect, MLOps & Agentic AI
Overview
You will design and implement end-to-end machine learning systems at scale, owning the full ML lifecycle from data pipelines to model deployment and Agentic AI integration. You will work hands-on with Kubernetes, Docker, and AWS in a collaborative team of engineers and data scientists. This role stands out for its focus on production-grade MLOps and the chance to shape AI infrastructure in a fast-moving environment.
What You'll Do8
- 1Design and build scalable data pipelines for model training and inference using Apache Spark and Kafka.
- 2Develop and maintain ML training and experimentation platforms using MLflow and Kubeflow.
- 3Deploy and manage models on AWS with Terraform and Helm for reproducible infrastructure.
- 4Monitor model performance and drift using Prometheus and Grafana, ensuring reliability in production.
- 5Integrate Agentic AI components into existing workflows, enabling autonomous decision-making.
- 6Drive the adoption of CI/CD for ML pipelines using GitHub Actions and Argo CD.
- 7Optimize inference latency and cost by tuning model serving with ONNX and TensorRT.
- 8Lead cross-functional initiatives to standardize MLOps practices across teams.
Requirements8
- 15+ years building and deploying ML systems in production with Python and TensorFlow or PyTorch.
- 23+ years hands-on experience with Kubernetes and Docker for containerized ML workloads.
- 32+ years designing data pipelines with Apache Spark and Kafka.
- 4Deep knowledge of MLOps tools such as MLflow, Kubeflow, and Airflow.
- 52+ years working with AWS services including SageMaker, ECS, and Lambda.
- 6Proven track record of deploying Agentic AI solutions in real-world applications.
- 7Strong understanding of model monitoring, drift detection, and retraining strategies.
- 8Experience with infrastructure as code using Terraform or CloudFormation.
Salary Insight
$60 - $65k per year
Location
Required Skills
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