AI Software Engineer

Smart IMS•Ann Arbor, MI

6h•Onsite

About The Position

As an AI Software Engineer, you will design, develop, and deploy AI-powered applications and services that leverage modern machine learning, Generative AI, agent frameworks, and enterprise software engineering practices. This role requires a strong combination of software development expertise and hands-on experience building, integrating, and supporting AI solutions in production environments. You will collaborate closely with software engineers, architects, product managers, and business stakeholders to deliver scalable, secure, and high-performing AI solutions. The ideal candidate will have experience developing AI-enabled applications, integrating Large Language Models (LLMs), implementing agent-based workflows, and applying modern software engineering principles to enterprise-scale solutions.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related field.
3-6 years of professional software development experience.
2+ years of hands-on experience developing AI, Machine Learning, or Generative AI applications.
Strong programming skills in one or more of the following languages: Python, Java, C#, TypeScript, or JavaScript.
Experience designing and developing REST APIs, microservices, and distributed systems.
Experience working with cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform (GCP).
Strong understanding of software engineering fundamentals including design patterns, testing methodologies, CI/CD pipelines, security practices, and observability.
Experience implementing Generative AI solutions using Large Language Models.
Knowledge of Prompt Engineering, Retrieval-Augmented Generation (RAG), embeddings, vector databases, and semantic search technologies.
Experience developing AI agents, intelligent workflows, and AI orchestration frameworks.
Strong understanding of API design, object-oriented programming, event-driven architectures, and scalable application development.
Experience with automated testing frameworks, source control systems, and modern development practices.
Familiarity with monitoring, logging, observability, and performance optimization techniques.
Experience implementing secure deployment practices, identity management, and access control models.
Strong analytical, problem-solving, troubleshooting, and communication skills.
Experience working within Agile software development environments.

Nice To Haves

Experience with LLM platforms such as OpenAI, Azure OpenAI, Anthropic Claude, Gemini, or similar AI services.
Experience building AI agents, MCP-based integrations, tool-calling frameworks, and multi-agent orchestration solutions.
Experience implementing Retrieval-Augmented Generation (RAG) architectures and vector database platforms.
Familiarity with prompt engineering methodologies, evaluation frameworks, and AI quality assessment techniques.
Experience with Docker, Kubernetes, Cloud Run, or similar containerization and orchestration technologies.
Experience developing AI assistants, copilots, conversational AI applications, and intelligent automation solutions.
Experience building document intelligence, knowledge management, search, and content retrieval platforms.
Exposure to AI-powered developer productivity tools and enterprise AI transformation initiatives.
Master’s degree in Computer Science, Artificial Intelligence, Software Engineering, Data Science, or a related discipline is preferred.

Responsibilities

Design, develop, test, and deploy AI-enabled applications and services.
Build and integrate Generative AI solutions using Large Language Models (LLMs).
Develop AI agents, orchestrated workflows, prompts, tools, and automation frameworks.
Implement Retrieval-Augmented Generation (RAG), semantic search, vector search, and knowledge retrieval solutions.
Develop APIs, microservices, and backend services supporting AI workloads and integrations.
Evaluate, benchmark, monitor, and optimize AI model performance, quality, latency, scalability, and cost.
Build automated testing, monitoring, observability, and reliability solutions for AI applications.
Collaborate with product owners and stakeholders to translate business requirements into technical solutions.
Implement responsible AI practices, security controls, privacy safeguards, and governance standards within AI solutions.
Contribute to architecture decisions, coding standards, and software engineering best practices.
Support production AI applications, troubleshoot issues, and participate in incident resolution activities.
Drive continuous improvements in AI development methodologies and operational processes.