We are particularly interested in candidates with hands-on experience architecting Retrieval-Augmented Generation (RAG) solutions and working with Model Context Protocol (MCP) to securely connect AI applications and agents with enterprise systems and data. Demonstrated hands-on experience building Retrieval-Augmented Generation (RAG) solutions using vector databases, embedding models, semantic search, document processing, and enterprise knowledge repositories.

Cloud AI Engineer

Location: Milford, MA
Work Arrangement: Remote
Schedule: Full-time, 37.5 hours/week
Contract: W2
Pay Rate:$86–$89/hr with benefits
Preference for candidates located in Massachusetts or those who are able to occasionally travel to Milford, Boston, or Middleborough, MA.

About the Role

Epitec is seeking an experienced Cloud AI Engineer to support the enhancement and management of a large-scale data platform. This is a senior technical role focused on building secure, scalable cloud and AI solutions that support complex data processing, analytics, and enterprise applications.
The ideal candidate will bring deep expertise across AWS cloud technologies, Generative AI, data engineering, AI/ML frameworks, infrastructure automation, Snowflake, and cloud security. This individual will play an important role in accelerating AI adoption, improving operational efficiency, optimizing cloud investments, and enabling enterprise-scale AI solutions.
We are particularly interested in candidates with hands-on experience architecting Retrieval-Augmented Generation (RAG) solutions and working with Model Context Protocol (MCP) to securely connect AI applications and agents with enterprise systems and data.

What You’ll Do

• Architect and implement Retrieval-Augmented Generation (RAG) solutions using vector databases, embedding models, semantic search, document processing, and enterprise knowledge repositories.
• Design, build, and deploy cloud-native AI/ML and Generative AI solutions that are scalable, secure, reliable, and production ready.
• Develop and integrate MCP servers and clients to securely connect AI agents and LLM-powered applications with enterprise systems, APIs, databases, and other data sources.
• Integrate LLM applications and AI agents with cloud services and enterprise platforms using secure integration patterns, MCP-based tool connectivity, APIs, and event-driven architectures.
• Apply enterprise AI security practices, including identity and access management, data privacy, prompt injection mitigation, content filtering, responsible AI principles, and governance controls across RAG, MCP, AI gateway, and agentic AI implementations.
• Optimize AI workloads through appropriate model selection, token usage management, caching, efficient retrieval strategies, latency reduction, and cloud resource optimization.
• Design, build, and deploy AWS-hosted data solutions, including large-scale data processing pipelines, advanced analytics, and computationally intensive workloads.
• Participate throughout the software development lifecycle (SDLC), including planning, requirements gathering, development, automated testing, deployment, and quality assurance.
• Work closely with data management and infrastructure teams to identify and implement optimal cloud-based solutions.
• Define and document best practices for application deployment, cloud architecture, and infrastructure maintenance.
• Ensure application performance, uptime, scalability, reliability, and code quality.
• Troubleshoot complex cloud, application, and data-platform incidents, conduct root-cause analysis, document findings, and implement preventative solutions.
• Identify and resolve potential system and data bottlenecks before they affect application or business performance.
• Provide cloud technical guidance, documentation, training, and mentorship to cross-functional teams.

AWS & Infrastructure Engineering

• Provision, configure, and manage AWS infrastructure using Terraform and AWS CloudFormation.
• Work extensively with AWS services including Amazon EKS, EC2, VPC, SNS/SQS, CloudWatch, ELB, Route 53, KMS, Secrets Manager, and PrivateLink.
• Configure virtual compute resources with an emphasis on optimal sizing, elasticity, auto-scaling, performance, reliability, and cost control.
• Design and manage AWS VPC networks, security groups, and private endpoints to enable secure connectivity between AWS, Snowflake, Tableau, and other enterprise platforms.
• Configure load balancing and failover routing using AWS ELB and Route 53 to support highly available data applications and BI endpoints.
• Securely manage cryptographic keys, credentials, and API secrets using AWS KMS and AWS Secrets Manager.
• Implement logging, monitoring, auditability, and proactive alerting through Amazon CloudWatch and related platform monitoring capabilities.
• Support cloud migration initiatives, including on-premises-to-AWS and cloud-to-AWS migrations.
• Apply the AWS Well-Architected Framework, with particular attention to Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability.

Snowflake & Data Engineering

• Provision and manage Snowflake resources alongside AWS infrastructure using Infrastructure as Code practices.
• Perform Snowflake administration, including performance tuning, query optimization, storage management, security, and access management.
• Configure and optimize Snowflake Virtual Warehouses, including sizing, elasticity, auto-scaling, performance, and cost controls.
• Implement Snowflake Role-Based Access Control (RBAC), network policies, and row- and column-level security to protect sensitive information.
• Build and maintain modern ETL/ELT pipelines supporting batch and streaming data ingestion.
• Optimize cloud data-processing frameworks for scalability, availability, performance, and throughput.
• Monitor platform performance and system integrity using Snowflake information schemas and AWS monitoring capabilities.

DevOps & CI/CD

• Develop and maintain automated CI/CD workflows using GitHub Actions to build, test, and deploy cloud infrastructure, data pipelines, and related applications.
• Apply Git-based development practices including branching strategies, pull requests, code reviews, and version control.
• Integrate GitHub Actions with AWS, Terraform, and Snowflake to support secure and automated deployments across multiple environments.
• Incorporate automated testing and quality assurance into cloud infrastructure and application deployment processes.

Business Intelligence

• Support Tableau Server/Cloud, including upgrades, performance tuning, administration, and access controls.
• Maintain effective integration between Tableau, AWS services, and enterprise data platforms.
• Support optimal dashboard and BI platform performance.
• Apply experience with Business Intelligence, Tableau, and Power BI within broader enterprise data environments.

Required Qualifications

• 10+ years of experience architecting, designing, developing, and implementing robust cloud and data solutions on the AWS platform.
• Demonstrated hands-on experience building Retrieval-Augmented Generation (RAG) solutions using vector databases, embedding models, semantic search, document processing, and enterprise knowledge repositories.
• Strong 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.
• Experience securely connecting AI agents and LLM applications with enterprise systems, APIs, cloud services, databases, and data sources.
• Strong understanding of enterprise AI security, including IAM, data privacy, prompt injection mitigation, content filtering, responsible AI, and governance.
• Deep technical experience with AWS services and foundational cloud components, including EKS, VPC, EC2, SNS/SQS, CloudWatch, and CloudFormation.
• Advanced proficiency with Infrastructure as Code, particularly Terraform and/or AWS CloudFormation.
• Experience developing and implementing cloud migration strategies, including on-premises-to-AWS and cross-cloud migrations.
• Working knowledge and practical application of the AWS Well-Architected Framework.
• Hands-on experience with GitHub and GitHub Actions, including designing and maintaining CI/CD workflows.
• Strong Git knowledge, including branching, pull requests, code reviews, and automated deployment practices.
• Demonstrated Snowflake administration experience, including performance tuning, security, RBAC, and access management.
• Strong proficiency in Python and advanced SQL.
• Experience with database architecture and ETL/ELT pipeline development.
• Experience with Business Intelligence platforms, including Tableau and/or Power BI administration.
• Strong troubleshooting, root-cause analysis, and problem-solving skills.
• Ability to collaborate effectively with Business Analysts, Developers, Data Scientists, infrastructure teams, and Subject-Matter Experts.
• Ability to successfully pass an FBI background check.

Preferred Qualifications

• Bachelor’s degree or equivalent experience in Computer Science, Information Technology, Mathematics, or a related technical discipline.
• AWS certification.

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