Job Title: Software Engineer/Technical Lead (Azure + AI)
Work Location: Remote
Duration :: Long Term Contract
NOTE :
Remote, but must be near for INPERSON INTERVIEW at San Francisco, Arlington, VA, Denver, CO, Chicago, Boston, NYC, Houston, Miami, Los Angeles, Seattle, Dallas, Minneapolis, Minnesota, Birmingham, MI or Irvine, CA.
Top Skills Required:
Most important requirement. The candidate should design scalable, secure, high-performance systems, make architecture decisions, own code quality, and guide technical direction.
Productionization of AI Use Cases :
Take AI PoCs/MVPs to production by building scaffolding, APIs, services, integrations, pipelines, environments, and release processes.
AI Application Engineering, Not Deep ML :
No deep model-building required. Need practical experience with RAG, agentic workflows, LLM/API integrations, evaluation, observability, and partnering with AI Engineers.
Azure + DevOps Delivery :
Strong Azure and CI/CD experience across infrastructure, security, identity, networking, environment management, and deployment readiness.
Enterprise Integration and Security :
Integrate AI applications with enterprise systems while meeting security, compliance, and enterprise standards.
Hands-On Builder Mindset :
Not a strategy-only architect. Must be able to code, review code, build services, set up repos/pipelines, and improve engineering practices.
We are seeking a Software Engineer to build and deploy AI-enabled applications in partnership with AI Engineers and product teams. This role focuses on application architecture, project scaffolding, enterprise integration, cloud implementation, and deployment readiness needed to move AI use cases from PoC to MVP and production.
The ideal candidate brings strong software engineering and cloud delivery experience, with practical exposure to AI solutions and common AI architecture patterns. This person does not need deep AI model-development expertise, but should understand how AI applications are structured, collaborate effectively with AI Engineers, and be adept at using modern AI productivity tools such as GitHub Copilot, Claude Code, and similar tools in a disciplined way to accelerate engineering delivery.
Your Impact:
- Architect and build high performance, scalable and secure AI solutions.
- Introduce and implement software engineering best practices (architecture/design patterns, building scalable, high performant and secure solutions).
- Responsible for code reviews and scaleability and security of production deployed systems
- Integrate AI solutions with systems to enable secure enterprise deployment.
- Select and apply the right technical patterns for AI solutions in partnership with AI Engineers.
- Scaffold projects, repositories, pipelines, environments, and shared services needed for delivery.
- Build core application components, APIs, data integrations, and deployment-ready services.
- Partner closely with DevOps and platform teams to ensure secure, scalable, and supportable deployments.
- Lead CI/CD, testing, release processes, and operational readiness for MVP and production solutions.
Skills & Experience :
- Strong software engineering fundamentals with a hands-on builder mindset and experience delivering production-grade applications.
- Some who has experience with and introduced software engineering best practices (architecture/design patterns, building scalable, high performant and secure solutions) to AI engineering teams and AI solutions.
- Practical experience working on AI solutions alongside AI Engineers, with understanding of common patterns such as RAG, agentic workflows, API-based model integration, and evaluation or observability needs.
- Strong Azure experience across infrastructure, platform services, security, identity, and networking.
- Experience with DevOps tooling, CI/CD pipelines, environment management, and secure deployment practices.
- Strong experience with open source and cloud technologies such as Neo4j, Cosmos DB, PostgreSQL, MongoDB, Kafka, Containers, K8s, FastAPI, and related application frameworks.
- Strong understanding of data platform choices, including when to use relational, NoSQL, graph, vector, and event-driven architectural patterns.
- Proficiency with GitHub workflows, branching strategies, pull requests, and modern AI productivity tools such as GitHub Copilot and Claude Code.
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