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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Artificial Intelligence Engineer - **Company:** Charter Global, Inc. - **Location:** New York, NY, United States - **Experience:** Expert - **Contract:** Temporary to permanent - **Skills:** SAP Sybase Adaptive Server Enterprise, Application Programming Interfaces (APIs), Agile Methodology, Data Analysis, JIRA, User Authentication, Cloud Engineering, Databases, Information Engineering, Data Integration, Extract Transform Load (ETL), Data Masking, Data Virtualization, IBM DB2, Linux, DevOps, Distributed Systems, Greenplum, Github, Apache Hadoop, IBM WebSphere MQ, Python (Programming Language), PostgreSQL, Unix Shell, Enterprise Messaging Systems, Microsoft SQL Server, MongoDB, Performance Tuning, Redis, Software Tools, Ansible, Systems Integration, Talend, Virtualization Technology, Enterprise Data Management, GitHub Copilot, Informatica Powercenter, Office365, Snowflake, Git, Containerization, Data Analytics, Apache Kafka, Bitbucket, Data Management, Dataiku, Terraform, Docker, Alteryx, Databricks - **Published:** September 4, 2026 - **Apply:** https://www.dice.com/job-detail/0c445a05-d14f-42e8-927e-a4b6c88dfbce ## About the Role * At least 8 years of hands-on experience in a similar system infrastructure engineer / developer role * At least 5 years of software developer experience with strong fundamentals in distributed system design, development, and deployment * Proficiency in Python and Unix shell scripting * Strong core infrastructure fundamentals (OS, networking, storage, virtualization, authentication/authorization, APIs, etc.) * Comfortable working within Agile/DevOps environments, with hands-on experience using Git, GitHub, Bitbucket, Jira, and related software delivery tooling * Working knowledge of modern AI-assisted engineering tools (e.g., GitHub Copilot, Microsoft 365 Copilot, Amp, Claude Code, or similar) and the ability to leverage them effectively and responsibly to improve engineering productivity * Working knowledge of one or more enterprise data, analytics, database, or messaging platforms such as Snowflake, Databricks, Kafka, PostgreSQL, MongoDB, MSSQL, Redis, Hadoop, Greenplum, Sybase ASE, DB2 UDB, or MQSeries * Strong analytical thinking, sound engineering judgment, and a practical, common-sense approach to problem solving; able to troubleshoot and resolve complex technical issues across multiple technology domains * A self-starter with the ability to work effectively both independently and as part of a team * Excellent verbal and written communication skills. Desired Skills: * Experience integrating, hardening, and operationalizing third-party/vendor platforms within complex enterprise environments, including alignment with security, authentication, networking, monitoring, automation, and governance standards through automation, tooling, and custom engineering solutions * Experience with containerization and orchestration technologies such as Docker and Kubernetes * Familiarity with public cloud platforms, cloud-native architectures, and associated security, governance, and compliance principles * Exposure to Infrastructure-as-Code and automation frameworks such as Ansible, Terraform, or equivalent tools * Experience designing, deploying, and supporting applications in large-scale, distributed, and highly available environments * Experience with system performance analysis, capacity planning, troubleshooting, and performance tuning * Working knowledge of one or more data virtualization, test data management, data masking, ETL, analytics, or data engineering platforms such as Denodo, Snowflake, Delphix, Informatica, Dataiku, Alteryx, Talend, or similar technologies ## Description * We are seeking a skilled and enthusiastic technologist to join our global central infrastructure data integration engineering team. * The successful candidate will be responsible for the engineering, automation, integration, and support of the client's data virtualization, data analytics, and test data management platforms. This includes developing and maintaining self-service capabilities and engineering tooling that enable secure, scalable, and seamless platform adoption across the Firm. Responsibilities will also include troubleshooting and escalation management, platform monitoring and performance tuning, operational automation, establishment of engineering best practices, and support for user onboarding and enablement activities. * The role offers opportunities to evaluate emerging technologies, perform proof-of-concept initiatives, and help shape the Firm's data integration and platform engineering strategy. The candidate will also contribute to the successful deployment and integration of vendor products within the client's complex enterprise environment, ensuring alignment with security, operational, scalability, and governance requirements. * The ideal candidate is a strong infrastructure engineer and software developer with hands-on experience building custom engineering solutions and automating operational workflows. They possess solid infrastructure fundamentals, strong analytical and problem-solving skills, a pragmatic engineering mindset, and proficiency in Python, Linux, and modern software delivery practices. ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Improving quality with Agentic AI with Rovo Dev and Xray](https://www.wearedevelopers.com/videos/2005-improving-quality-with-agentic-ai-with-rovo-dev-and-xray) - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [The Software Engineer 2030: From Coder To AI Orchestrator? - Patrick Schnell](https://www.wearedevelopers.com/videos/1825-the-software-engineer-2030-from-coder-to-ai-orchestrator-patrick-schnell) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Transforming Software Development: The Role of AI and Developer Tools](https://www.wearedevelopers.com/magazine/527-transforming-software-development-the-role-of-ai-and-developer-tools) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai)