Mid Level Cloud AI Architect Jobs
Mid level cloud ai architect jobs go to professionals ready to own end-to-end architecture decisions, drive cross-functional AI deployments, and guide junior engineers with limited oversight. Roles are concentrated across Technology & Software, Banking & Financial Services, and Investment & Asset Management, with a strong mix of remote and hybrid settings, and employers like Goldman Sachs, JPMorganChase, and Ampcus hiring at this level now.
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Job Title: AI Coding Tools Engineer (Sr. AI Full-Stack Engineer with Coding Tools [Claude Code, Codex] and agentic AI)
Location: Chantilly, VA
Notes
- Windows and Linux experience (ideally Admin experience) and capable of configuring different coding tools in internal hosted Windows and Linux environments.
- Project management and communications skills to be able to manage different projects and communicate with different parties, with the client, for supporting the operation and roll-out of a variety of coding assistant tools.
- Familiarity with some of the existing coding assistant tools (e.g., Claude Code, Cline, Codex).
Position Description: AI Coding Tools Engineer
The AI Coding Tools Engineer (GenAI & Agentic Systems) evaluates, pilots, operationalizes, maintains, and scales adoption of secure AI coding assistant tools across the enterprise.
Key Responsibilities
AI Coding Assistant Evaluation
- Conduct structured evaluations of leading enterprise AI coding assistants, including features, model performance, security, integration complexity, and developer ergonomics.
AI Coding Assistant Piloting, Launching & Adoption Support
- Integrate coding assistants into IDEs (VS Code), terminals/CLI workflows, and source-control ecosystems.
- Configure AI guardrails, including content filtering, prompt-shielding, and role-based access controls aligned with enterprise and NIST requirements.
- Work with Security Team to prepare ATO evidence, including SSP updates, control narratives, risk registers, and continuous-monitoring artifacts.
- Work with Training and Enablement Team to develop training, compliance, and end-user/best-practice guides.
- Support Coding Assistant “office hours” to support developer enablement and accelerate adoption.
- Collect user feedback, track adoption metrics, and iteratively refine usage patterns for different developer roles.
AI Coding Assistant Operations & Maintenance Support
- Monitor platform health and guardrail performance.
- Support analysis and remediation of integration or platform issues impacting the coding assistants.
- Track, evaluate, and support application of coding assistant updates and patches.
Required Qualifications
- Minimum 2 years of experience with leading AI Coding Assistants.
- Minimum 3–5 years of experience in software engineering, developer experience engineering, platform engineering, or related roles.
- Experience integrating or evaluating LLM-powered developer tools (e.g., code completion, chat-based programming assistance, test generation, refactoring tools).
- Understanding of NIST compliance and government cloud environments.
- Familiarity with enterprise DevSecOps practices, modern IDEs, and secure software development lifecycles.
Preferred Qualifications
- Direct experience leading enterprise adoption of AI coding assistants (pilot design, rollout planning, governance alignment).
- Hands-on experience with Amazon Bedrock GovCloud, Azure OpenAI (Gov), and/or Vertex AI (Assured Workloads) for production workloads.
- Experience building multi-step agent workflows on Bedrock Agents, implementing Bedrock Guardrails, or building RAG/semantic-search systems via Vertex AI Search.
- Experience supporting ATO artifacts and design/testing of controls.
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Top Industries Hiring
- Technology & Software
- Banking & Financial Services
- Investment & Asset Management
- Education
- Fintech
Mid Level Cloud AI Architect Jobs: Frequently Asked Questions
How do I get a mid level cloud ai architect job?
Position your experience around ownership, not just participation. Highlight systems you designed independently, decisions you made under real constraints, and the measurable outcomes that followed. Recruiters at this level want evidence you can run a workstream with minimal hand-holding, so lead with architecture contributions, certifications like AWS Solutions Architect or Google Professional Cloud Architect, and any cross-team collaboration you drove.
Which companies hire mid level cloud ai architects?
Companies hiring mid level cloud ai architects right now include Goldman Sachs, JPMorganChase, and Ampcus, based on current listings on Migrate Mate as of September 2026. Hiring at this level covers large enterprises modernizing legacy infrastructure, cloud-native software firms scaling AI products, and technology consultancies staffing client delivery teams.
Are there remote mid level cloud ai architect jobs?
Yes, and remote availability at this level is strong. About 20% of mid level cloud ai architect openings are remote or hybrid as of September 2026, reflecting the infrastructure-as-code and cloud-native nature of the work. Fully on-site roles tend to appear in regulated industries like finance and healthcare where data governance requirements drive in-person collaboration.
How do I move up to a mid level cloud ai architect role?
The path from entry level to mid level centers on building demonstrable ownership. Start by taking full responsibility for discrete features or pipeline components rather than just contributing to them. Deepen your expertise in at least one major cloud platform, pursue a professional-level certification, and document the measurable impact of your architectural choices. Consistent delivery with increasing scope is what signals readiness for a mid level title.
Which industries hire the most mid level cloud ai architects?
Mid Level cloud ai architect roles concentrate in Technology & Software, Banking & Financial Services, and Investment & Asset Management, based on current listings on Migrate Mate as of September 2026. These sectors drive hiring at this level because they are actively scaling cloud infrastructure, deploying AI and machine learning systems at production scale, and need architects who can own technical execution without daily senior oversight.