Bachelor's degree in Computer Science, Software Engineering, or related field, or equivalent experience.
Proficiency in backend languages like C#/.NET, Python, or TypeScript/Node.js.
Hands-on experience with AI-powered features in production, including LLM integrations and workflow automation.
Experience designing APIs and service integrations in distributed systems.
Familiarity with cloud-hosted AI services such as Azure OpenAI or Azure AI Search.
Understanding of evaluation and reliability considerations for AI systems in production.
3+ years of professional software development experience, including end-to-end application monitoring.
Responsibilities
Design and deliver AI-powered product features from requirements to post-release optimization.
Build and maintain retrieval-augmented generation (RAG) workflows for enterprise data.
Implement backend services integrating large language models into enterprise products via APIs.
Develop AI-assisted workflows that safely interact with enterprise data and external systems.
Create automated tests and operational runbooks for AI features to ensure reliability.
Analyze production behavior and user feedback to enhance answer quality and efficiency.
Collaborate with cross-functional teams to define use cases and rollout plans.
Benefits
Collaborative work environment with cross-functional teams.
Opportunity to take full ownership of AI feature delivery.
Focus on practical engineering trade-offs in real production settings.
Engagement with cutting-edge AI technologies and cloud services.
Professional growth through contributions to engineering standards and best practices.
Full Job Description
As a Mid-Level AI Software Developer, you will own and deliver production-ready AI features end-to-end. You will work across LLM integrations, retrieval-augmented generation (RAG) systems, and AI-driven workflows, building reliable backend services and applications that directly impact enterprise customers.
This role is ideal for an experienced software engineer who has shipped AI-powered features in production and is ready to take full ownership of feature delivery, including performance, reliability, and cost efficiency. We are looking for candidates who are practical, collaborative, and comfortable making sound engineering trade-offs in real production environments.
Responsibilities:
Design and deliver AI-powered product features end-to-end, from requirements and solution design through implementation, testing, deployment, and post-release optimization.
Build and maintain retrieval-augmented generation (RAG) workflows that ground LLM responses in enterprise data, including application-side retrieval design, response grounding, and retrieval tuning.
Implement application and backend services that integrate large language models and AI services into enterprise products through well-designed APIs and service boundaries.
Develop AI-assisted workflows and agent-style automations that interact safely with enterprise data, product capabilities, and external systems.
Create and maintain automated tests, evaluation practices, monitoring, and operational runbooks for AI-enabled features so reliability is designed in from the start.
Analyze production behavior, user feedback, and telemetry to improve answer quality, latency, reliability, and cost efficiency. Make and communicate trade-offs between model quality, latency, and cost.
Collaborate with product, UX, data, QA, and platform teams to define use cases, acceptance criteria, evaluation methods, and rollout plans.
Contribute to engineering standards through code reviews, technical design discussions, and shared best practices focused on testing, observability, security, and performance.
What Success Looks Like
Owns AI-powered features end-to-end in production, from implementation through post-release improvement.
Uses telemetry, user feedback, and evidence to improve answer quality, latency, reliability, and cost efficiency.
Makes sound trade-offs between quality, latency, and cost, and can clearly explain those decisions to technical and non-technical stakeholders.
Delivers reliable, well-tested features with appropriate monitoring, evaluation, and operational readiness.
Collaborates effectively across engineering, product, UX, data, QA, and platform partners to ship high-quality releases.
Qualifications & Experience
Education
Bachelor's degree in Computer Science, Software Engineering, or a related technical field, or equivalent demonstrated professional experience.
Skills & Requirements
Technical Skills
Proficiency in one or more backend languages and frameworks such as C#/.NET, Python, or TypeScript/Node.js.
Hands-on experience building and shipping AI-powered features in production, such as LLM integrations, retrieval-based search, conversational assistants, intelligent agents, or workflow automation.
Experience designing APIs and service integrations in distributed or service-oriented systems.
Working knowledge of relational databases, including schema design, query fundamentals, and indexing concepts.
Familiarity with cloud-hosted AI services such as Azure OpenAI, Azure AI Search, Azure Bot Service, Microsoft Agent Framework, or comparable platforms.
Understanding of evaluation, monitoring, and reliability considerations for AI systems in production.
Understanding of cost-quality trade-offs in LLM systems, including examples such as model selection, token efficiency, caching, or reducing unnecessary API calls.
Proficiency with Git, code review, CI/CD pipelines, and modern software development practices.
Experience
3+ years of professional software development experience, including production experience designing, building, testing, deploying, and monitoring applications end-to-end.
Experience shipping at least one production-deployed AI-powered feature with measurable customer impact within the past 18-24 months. This can be part of the 3+ years of total experience.
Experience working with real production constraints such as operational reliability, data governance, and cost-performance trade-offs in AI-enabled systems.
Experience working in Agile/Scrum and collaborative cross-functional development environments.
