Platform Engineer

Saicon; Port Reading (NJ); On-site; USD 103,320 - 110,208; Full time

Job summary

Saicon is looking for a GenAI DevOps expert for a hybrid role based in Port Reading, NJ. The ideal candidate will own and enhance CI/CD processes for AWS-based GenAI platforms, ensuring secure deployments and infrastructure management.

Applicants must show expertise in maintaining pipelines and databases, leveraging AWS CDK, and managing secure environments. Strong knowledge of security practices, along with skills in Python and Terraform, is a plus. This role supports a dynamic and regulated environment.

Qualifications

• Experience maintaining CI/CD pipelines and databases.
• Experience with AWS CDK for GenAI.
• Ability to manage and scale secure infrastructure.
• Experience in a regulated environment.

Responsibilities

• Own and improve CI/CD and infrastructure patterns for AWS-based GenAI platforms.
• Provision and operate graph databases, Redis, and handle security concerns.
• Support container/image build and deployment workflows across repositories.

Skills

AWS CDK

Terraform

Python

Infrastructure management

Information Security

Tools

GitHub

Bitbucket

CloudFormation

Neo4j

Amazon Neptune

Redis

Job description

US CITIZENS AND GREEN CARD ONLY. No 3rd party

Role: GenAI DevOps

Term: 6-12 months Contact to Hire

Location: Woodbridge, NJ – Hybrid 3 days onsite

Visa: Green Card or US Citizen

Pay: $75/Hr. - $80/Hr. W-2

Key responsibilities:

• Own and improve CI/CD and infrastructure patterns for AWS-based GenAI agent platforms using CDK, GitHub/Bitbucket Pipelines, OIDC, and CloudFormation.
• Evolve secure multi-repo GenAI deployment architecture where shared infrastructure (Bedrock access, agent runtimes, gateways, graph databases, Redis caches) is provisioned centrally and consumed through SSM parameter contracts.
• Provision and operate graph databases (Neo4j, Amazon Neptune, or equivalent) supporting knowledge graphs and agent reasoning context — covering schema management, backup/restore, scaling, query tuning, and secure access.
• Provision and operate Redis for agent session state, semantic caching, rate limiting, pub/sub, and short-term memory — covering cluster sizing, replication, failover, persistence, and TLS/auth hardening.
• Implement secure deployment role chains: OIDC deploy roles, CDK bootstrap deploy roles, and scoped CloudFormation execution roles.
• Design and troubleshoot least-privilege IAM policies across CDK, CloudFormation, Lambda, Bedrock, AgentCore runtime, agent gateway, and graph/Redis access.
• Support container/image build and deployment workflows to shared ECR repositories across agent repos.
• Improve environment bootstrapping, deployment repeatability, drift detection, rollback, and operational validation — including stateful data layers.
• Partner with InfoSec to harden deployment flows and document secure GenAI platform standards, including data-at-rest and in-transit controls for graph and Redis stores.
• Troubleshoot complex deployment issues across IAM, CloudFormation, OIDC, VPC connectivity, ECR, API Gateway, Bedrock AgentCore, graph/Redis connectivity, and observability.

Must Have Experience:

• Maintain pipelines and DBs
• CDK for GenAI
• Terraform a plus as well
• Python
• Manage and scale infrastructure
• Working in a secure way/constrained environment – The environment is regulated
• Infosec and security – partner with

Strongly preferred

• Experience with AWS Bedrock and/or AgentCore-style orchestration/runtime patterns.
• Experience with agent-to-agent or service-to-service security design for GenAI platforms.
• Experience securing AI/LLM platforms, agent orchestration layers, or API-driven enterprise GenAI platforms.
• Experience integrating graph databases and Redis as supporting data layers for GenAI agents (knowledge graphs, semantic caching, session memory).
• Experience with enterprise guardrails, audit logging, and security review processes for AI workloads.
• Familiarity with SSM contract-based repo integration patterns.
• Experience with private subnet/VPC endpoint architectures for Lambda, Bedrock, graph/Redis endpoints, or containerized GenAI runtimes.

Experience supporting regulated or security-conscious enterprise environments running GenAI workloads.