Cloud Platform Engineer
UNIVERSAL Technologies is seeking a Cloud Platform Engineer for an onsite role in Brooklyn, NY with strong AWS, AI/ML, and data platform expertise to support a high-priority, agency-wide initiative focused on reducing payment error rates and strengthening program integrity. Active LinkedIn preferred. This role will design, deploy, and support scalable, secure cloud infrastructure and AI-enabled data solutions that enhance eligibility accuracy, automation, compliance, and data-driven decision-making in a regulated enterprise environment.
WHO WE ARE
UNIVERSAL Technologies is a Women-Owned (M/WBE) IT consulting and professional services firm delivering innovative technology solutions to government and enterprise clients. We specialize in cloud engineering, data analytics, artificial intelligence, automation, and application development to help organizations improve performance, compliance, and service delivery.
WHAT WE OFFER
Engagement on mission-critical, high-visibility government initiatives
Hands-on work with AWS cloud, AI/ML, and data platforms
Collaborative environment with experienced cloud, data, and AI professionals
Commitment to professional growth and continuous learning
MANDATORY REQUIREMENTS
Candidates who do not meet the mandatory requirements will not be considered.
Minimum 7 years of hands-on experience supporting AWS environments, including EC2, RDS, S3, CloudWatch, CloudTrail, IAM, KMS, AWS Backup, and Lambda
Minimum 7 years of experience administering Linux/Unix systems and developing automation scripts using Bash, Shell, or Python
Minimum 7 years of experience implementing Infrastructure as Code (IaC) and automation using CloudFormation, Terraform, and Ansible
Minimum 7 years of experience designing and supporting AWS networking architectures, including VPCs, subnets, NACLs, security groups, Route 53, and multi-AZ environments
Minimum 5 years of experience building and supporting CI/CD pipelines using Jenkins and IaC for deploying AI agents and ML models into production
Minimum 5 years of experience supporting MLOps workflows using container-based platforms such as Kubernetes, ECS, or EKS
Minimum 4 years of experience architecting and maintaining scalable data processing workflows using AWS managed services and Python, including PySpark
Minimum 4 years of experience designing data architectures and implementing ETL/ELT pipelines
Minimum 4 years of experience working with AWS AI/ML services such as SageMaker, Bedrock, and vector databases including OpenSearch
Strong understanding of machine learning algorithms, NLP concepts, and deep learning frameworks such as TensorFlow, PyTorch, or Hugging Face
Strong communication and documentation skills within cross-functional technical teams
SCOPE OF SERVICES
Monitor database and system performance using AWS CloudWatch metrics, alarms, and logs, and proactively troubleshoot issues
Develop, deploy, and optimize AI/ML solutions using AWS services such as SageMaker and Bedrock, supporting model training, inference, and production integration
Automate operational tasks using AWS Lambda, Systems Manager (SSM), and Infrastructure-as-Code tools
Design, build, and maintain scalable, fault-tolerant data processing and analytics workflows using AWS services including API Gateway, S3, EC2, RDS, Lambda, Glue, Athena, DynamoDB, EMR, Kinesis, and DataSync
Design and integrate agentic AI systems, including LLM-based agents and multi-agent workflows using frameworks such as LangChain and LangGraph
Implement ETL/ELT pipelines and data architectures supporting machine learning, analytics, and intelligent agent-based applications
Support CI/CD pipelines for AI models and data workflows using Jenkins and container platforms such as ECS, EKS, or Kubernetes
Apply security best practices across AI and data platforms, including IAM least-privilege access, encryption, audit logging, and compliance controls
Maintain technical documentation for AI architectures, data pipelines, infrastructure configurations, and operational runbooks
UNIVERSAL Technologies is an equal opportunity employer.