Duration: 7 months to start

About the Role
The client is hiring an experienced Cloud AI Engineer as part of a team that is in the process of enhancing and managing a system-wide criminal justice data platform. The first type in the nation, the platform will integrate data from police departments, the courts, and the state's custodial institutions to better understand and improve pathways through the criminal justice system, reduce recidivism, and improve public reporting of criminal justice data.

Who We're Looking For
We are seeking an experienced cloud AI engineer who possesses deep expertise in cloud technologies, data engineering, AI/ML frameworks, infrastructure automation, and cloud security, along with a strong understanding of emerging AI technologies and industry best practices. The Cloud AI Engineer will play a critical role in accelerating AI adoption, improving operational efficiency, optimizing cloud investments, and enabling innovative, enterprise-scale AI solutions that drive business value.

Objectives of This Role

• Work in tandem with our data management team and infrastructure team to identify and implement the most optimal cloud-based solutions.
• Define and document best practices and strategies regarding application deployment and infrastructure maintenance.
• Provide guidance, thought leadership, and mentorship to team members to build their cloud competencies.
• Ensure application performance, uptime, and scale, maintaining high standards for code quality and thoughtful design.
• Manage cloud environments in accordance with security guidelines.

Responsibilities

• Proven experience architecting and implementing retrieval-augmented generation (RAG) solutions using vector databases, embedding models, semantic search, and enterprise knowledge repositories to deliver accurate, context-aware, and scalable generative AI applications.
• Strong knowledge of enterprise AI security practices, including identity and access management, data privacy, prompt injection mitigation, content filtering, responsible AI principles, and governance controls across AI Gateway, MCP, RAG, and agentic AI implementations.
• Experience in optimizing AI workloads through intelligent model selection, token usage management, caching, efficient retrieval strategies, latency reduction, and cloud resource optimization while maintaining application performance and reliability.
• Ability to integrate LLM-powered applications and AI agents with enterprise systems, cloud services, databases, and APIs using secure integration patterns, MCP-based tool connectivity, and event-driven architectures
• Implement technical efforts to design, build, and deploy AWS-hosted data solutions, including large-scale data processing pipelines, computationally intensive statistical modeling, and advanced analytics.
• Participate in all aspects of the software development lifecycle (SDLC) for AWS solutions, including planning, requirements gathering, development, automated testing, and quality assurance.
• Expertly troubleshoot incidents, identify root causes, fix and document complex issues, and implement preventive measures to ensure system stability.
• Educate cross-functional teams on the implementation of new cloud-based data initiatives, providing associated training and technical documentation when necessary.
• Demonstrate exceptional problem-solving skills, with a proven ability to see, predict, and resolve data bottlenecks or system issues before they impact business productivity.
• Utilize Terraform to provision, manage, and orchestrate AWS infrastructure and Snowflake resources efficiently.
• Oversee and implement upgrades, performance tuning, and access controls within Tableau Server/Cloud, ensuring seamless integration with AWS services and optimal dashboard performance.
• Configure and manage virtual compute resources (AWS EC2 and Snowflake Virtual Warehouses), focusing on optimal configuration, sizing, elasticity, auto-scaling, and cost control.
• Implement and maintain rigorous security configurations, including AWS IAM, Snowflake Role-Based Access Control (RBAC), network policies, and Row/Column-level security to protect sensitive data.
• Perform data platform administrative functions across AWS and Snowflake, ensuring smooth operations, query optimization, storage management, and compliance with best practices.
• Configure and manage load balancing strategies and failover routing (AWS ELB/Route 53) to ensure high availability and fault tolerance across data applications and BI endpoints.
• Design and manage AWS VPC networks, security groups, and private endpoints (AWS PrivateLink) to ensure secure, efficient, and private connectivity between AWS, Snowflake, and Tableau.
• Respond swiftly to data pipeline and platform failures, executing root-cause analysis and implementing robust engineering solutions to prevent recurrence.
• Utilize and update cloud data processing frameworks and modern ETL tools to manage streaming or batch data ingestion, optimizing performance and throughput.
• Securely manage cryptographic keys, database credentials, and API secrets using AWS KMS and AWS Secrets Manager to protect data at rest and in transit.
• Implement and manage comprehensive logging and monitoring mechanisms using Amazon CloudWatch and Snowflake information schemas to ensure system integrity, auditability, and proactive alerting

Required Skills and Qualifications.

• 10 or more years of experience in architecting, designing, developing, and implementing robust data and cloud solutions on the AWS platform.
• Demonstrated expertise in building Retrieval-Augmented Generation (RAG) solutions using vector databases, embedding models, semantic search, document processing, and enterprise knowledge repositories.
• Strong hands-on experience designing, developing, and deploying cloud-native AI/ML and Generative AI solutions
• Hands-on experience with Model Context Protocol (MCP), including developing and integrating MCP servers and clients to securely connect AI agents and LLM applications with enterprise systems, APIs, and data sources.
• Deep technical experience provisioning and managing foundational AWS components, including Amazon Elastic Kubernetes Service (EKS), VPC, Amazon SNS/SQS, Amazon CloudWatch, AWS CloudFormation, and Amazon EC2.
• Proven track record executed in cloud migration strategies, including On-Premises to AWS and cross-cloud (Cloud Provider to AWS) migrations.
• Advanced proficiency with Infrastructure as Code tools, specifically Terraform or AWS CloudFormation, to automate infrastructure deployment.
• Thorough understanding and practical application of the AWS Well-Architected Framework pillars (Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability).
• Hands-on experience with GitHub and GitHub Actions, including developing, configuring, and maintaining automated CI/CD workflows for building, testing, and deploying cloud infrastructure and data pipelines.
• Proficiency in Git-based version control, branching strategies, pull requests, code reviews, and integrating GitHub Actions with AWS, Terraform, and Snowflake for secure, automated deployments across multiple environments.
• Demonstrated hands-on experience in Snowflake administration, including performance tuning, security, and access management; strong proficiency in Python and advanced SQL;
• Specialized experience across multiple data domains, including Database Architecture, ETL/ELT pipeline development, Business Intelligence (BI), Power BI and Tableau Administration
• Demonstrated ability to partner and collaborate effectively with multidisciplinary teams, including Business Analysts, Developers, Data Scientists, and Subject-Matter Experts.
• Ability to successfully pass an FBI background check

Preferred skills and qualifications

• Bachelor’s degree (or equivalent) in computer science, information technology, or mathematics
• AWS certification

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