Software Engineer

Key Responsibilities

• 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.

Required Qualifications

• 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/API integrations, AI evaluation, and 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.
• Strong understanding of data architecture and the appropriate use of: relational databases, NoSQL databases, graph databases, vector databases, and event-driven architectures.
• Experience using 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.