Software Engineer

• Architect and build scalable, secure, and high-performance AI-enabled applications.
• Make technical and architectural decisions and help guide the engineering direction of AI solutions.
• Take AI Proofs of Concept and MVPs into production by building application scaffolding, APIs, services, integrations, pipelines, environments, and release processes.
• Introduce and implement software engineering best practices, including architecture and design patterns, code quality, scalability, security, performance, and maintainability standards.
• Conduct code reviews and take ownership of the quality, security, reliability, and scalability of production systems.
• Build core application components, APIs, data integrations, and deployment-ready services.
• Integrate AI applications with enterprise systems while meeting security, compliance, and enterprise standards.
• Partner with AI Engineers to select and implement appropriate AI application patterns.
• Support AI solutions involving RAG, agentic workflows, LLM/API integrations, evaluation, and observability.
• Collaborate with DevOps and platform teams to ensure secure, scalable, supportable, and production-ready deployments.
• Lead CI/CD, automated testing, release management, deployment, and operational-readiness activities.
• Establish and improve repositories, branching strategies, pull-request processes, development workflows, and engineering standards.
• Use modern AI productivity tools, including GitHub Copilot, Claude Code, or similar tools, to improve engineering delivery.

We are seeking a highly skilled, hands-on Software Engineer to architect, build, and deploy scalable, secure, and high-performance AI-enabled applications.

This role will work closely with AI Engineers, product teams, DevOps, and platform teams to move AI use cases from Proof of Concept (PoC) and MVP stages into secure, production-ready enterprise applications.

The ideal candidate will have strong software engineering, application architecture, Azure cloud, DevOps, enterprise integration, and production delivery experience. Deep AI/ML model development experience is not required; however, candidates must have practical exposure to AI application patterns such as RAG, agentic workflows, LLM/API integrations, AI evaluation, and observability.

This is a hands-on engineering role. We are looking for someone who can write and review code, build APIs and services, establish project repositories and CI/CD pipelines, make architecture decisions, and improve engineering practices., * Strong hands-on software engineering experience delivering production-grade applications.

• Demonstrated experience designing scalable, secure, reliable, and high-performance systems.
• Strong experience with software architecture, design patterns, code quality, and engineering best practices.
• Experience making technical and architectural decisions for complex enterprise applications.
• Practical experience working on AI-enabled applications in partnership with AI Engineers.
• Understanding of AI application patterns, including:
• Retrieval-Augmented Generation (RAG)
• Agentic workflows
• LLM and API integrations
• AI evaluation
• AI observability
• Strong Microsoft Azure experience, including infrastructure, platform services, security, identity, networking, and environment management.
• Strong experience with DevOps tools and CI/CD pipelines.
• Experience with automated testing, deployment pipelines, release processes, and secure deployment practices.
• Strong experience with one or more of the following technologies:
• Neo4j
• Azure Cosmos DB
• PostgreSQL
• MongoDB
• Kafka
• Containers
• Kubernetes
• FastAPI
• Related cloud and application frameworks
• Strong understanding of data architecture and the appropriate use of:
• Relational databases
• NoSQL databases
• Graph databases
• Vector databases
• Event-driven architectures
• Strong experience with GitHub workflows, branching strategies, pull requests, and code reviews.
• Experience using GitHub Copilot, Claude Code, or similar AI-assisted engineering tools.
• Strong communication and collaboration skills.
• Must have a hands-on builder mindset and be comfortable coding, reviewing code, building services, creating repositories and pipelines, and improving engineering practices.

Preferred Qualifications

• Experience taking AI applications from PoC or MVP stages into production.
• Experience integrating AI applications with enterprise platforms and systems.
• Experience with enterprise security, identity, networking, compliance, and governance requirements.
• Experience working closely with AI Engineering, product, DevOps, and cloud platform teams.
• Experience supporting production AI applications and improving their reliability, performance, observability, and scalability.

Location Requirement

This position is remote; however, candidates must currently reside near one of the following approved metropolitan areas:

San Francisco, CA; Arlington, VA; Denver, CO; Chicago, IL; Boston, MA; New York City, NY; Houston, TX; Miami, FL; Los Angeles, CA; Seattle, WA; Dallas, TX; Minneapolis, MN; Birmingham, MI; or Irvine, CA.

Candidates should be available for a virtual interview and may be required to attend an in-person interview.

Skills: software,architecture,enterprise,databases,application,devops,interview,code,security,pipelines