Cloud Engineer

•  BS/MS in Computer Science (or equivalent)
•  AWS Certified Cloud Engineer, Architect, Administrator Certifications required
•  7+ years of advanced knowledge and experience as an AWS Cloud Engineer in all core services and offerings. AWS experience a plus
•  15+ years of advanced knowledge and experience of Microsoft Technologies such as, Windows server and Linux based servers, enterprise system support experience and strong background in systems engineering and administration for both operating systems
•  15+ years of advanced knowledge and experience with enterprise scale Windows technologies such as Server platforms, Desktop platforms, Exchange Environments, Active Directory, IIS, Windows Clustering, Virtualization and Collaboration tools. AWS Certification or equivalent experience preferred
•  Working knowledge of  DevOps-like work or experience in a real time operational role
•  Advanced knowledge of analytics and AI/ML platform services across AWS, Azure, and GCP (e.g., AWS SageMaker/Bedrock, Azure Machine Learning/Azure OpenAI, Google Vertex AI) and how to operate them securely at enterprise scale.
•  Experience enabling teams to build and deploy ML/AI solutions by providing reusable platform capabilities (reference architectures, templates, SDK/CLI standards, self-service onboarding, and guardrails) rather than only project-specific implementations.
•  Hands-on experience operationalizing ML/AI workloads on cloud platforms (AWS/Azure/GCP): managed training/inference, batch vs real-time serving, feature/metadata management, model registry, and cost/performance optimization.
•  Strong MLOps/platform engineering experience: CI/CD for ML and GenAI, automated validation gates, reproducible pipelines, environment promotion, artifact/version management, and production monitoring (drift, data quality, latency, cost) using cloud-native and/or enterprise tooling (e.g., Azure DevOps/GitHub Actions, SageMaker Pipelines, Vertex AI Pipelines, MLflow, Terraform).
•  GenAI platform experience (AWS/Azure/GCP): deploying and governing LLM applications using managed services (e.g., Bedrock/Azure OpenAI/Vertex AI), RAG architectures, embeddings and vector databases/search, prompt/version management, and evaluation/guardrails for safety and groundedness.
•  Responsible AI + governance experience for regulated environments: PHI/PII protections, access controls, encryption and key management, audit logging, model/endpoint risk assessments, bias/fairness considerations, and policy enforcement aligned to HIPAA and secure SDLC.
•  Strong data engineering foundations that support AI platforms: standardized data ingestion/ETL/ELT, data quality/lineage, dataset and feature pipeline design, schema/version management, and integration with lake/lakehouse platforms (e.g., S3/ADLS/GCS with Spark/Databricks/BigQuery/Synapse) for feature and training data readiness.
•  Experience operating scalable training/inference platforms (GPU/accelerated workloads): capacity planning/quotas, cluster or managed compute configuration, distributed training concepts, performance tuning, and chargeback/showback in cloud environments.