Mid-Level AI Engineer / AI Developer - 3041033

Apex Systems LLC
Ann Arbor, United States of America
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Intermediate

Job location

Ann Arbor, United States of America

Tech stack

Java
JavaScript
API
Agile Methodologies
Artificial Intelligence
Amazon Web Services (AWS)
Software Applications
Application Performance Management
Automated Storage and Retrieval Systems
Automation of Tests
Azure
C Sharp (Programming Language)
Cloud Computing
Cloud Engineering
Code Review
Information Systems
Computer Programming
Software Design Patterns
Monitoring of Systems
Identity and Access Management
Python
Machine Learning
Object-Oriented Software Development
Performance Tuning
Search Technologies
Software Construction
Software Engineering
Systems Integration
TypeScript
Data Logging
Google Cloud Platform
Spring Cloud
Large Language Models
Multi-Agent Systems
Prompt Engineering
Software Security
Generative AI
Backend
Containerization
AI Platforms
Kubernetes
Information Technology
Low Latency
Deployment Automation
REST
Software Version Control
Docker
Microservices

Job description

We are seeking a Mid-Level AI Engineer to design, develop, deploy, and support AI-powered applications and services that deliver business value at scale. This role works closely with software engineers, product managers, architects, data teams, and business stakeholders to build enterprise-grade AI solutions using modern machine learning techniques, generative AI platforms, agent frameworks, and cloud-native software engineering practices., AI Application Development

  • Design, develop, test, deploy, and maintain AI-enabled applications and services.
  • Build and integrate Generative AI solutions using Large Language Models (LLMs) and foundation models.
  • Develop AI-powered copilots, assistants, conversational experiences, and enterprise automation solutions.
  • Engineer scalable AI workflows that leverage prompt orchestration, tool usage, and agent-based interactions.
  • Translate business requirements into secure, reliable, and maintainable technical solutions.

Generative AI & Agent Engineering

  • Develop AI agents, tools, workflows, and orchestration frameworks.
  • Design and implement Retrieval-Augmented Generation (RAG), semantic search, and enterprise knowledge retrieval solutions.
  • Build and maintain MCP (Model Context Protocol) integrations, tool-calling frameworks, and multi-agent systems.
  • Create and optimize prompts, system instructions, and evaluation processes to improve AI performance and reliability.
  • Evaluate model behavior and implement strategies for improving response quality, grounding, and accuracy.

Software Engineering & Platform Development

  • Build APIs, microservices, and backend services that support AI workloads.
  • Develop reusable components and services that enable scalable AI capabilities across the organization.
  • Apply software engineering best practices including code reviews, testing, documentation, and version control.
  • Participate in architectural design and technical decision-making for AI solutions and platforms.
  • Ensure AI services meet performance, scalability, security, and availability requirements.

AI Operations & Optimization

  • Evaluate, benchmark, and optimize AI models for quality, latency, reliability, and cost efficiency.
  • Implement automated testing, monitoring, logging, and observability for AI applications.
  • Support AI systems in production environments and participate in troubleshooting, incident response, and root cause analysis.
  • Monitor application performance and implement continuous improvements based on usage and operational insights.
  • Establish evaluation frameworks and performance benchmarks for AI systems.

Governance, Security & Compliance

  • Implement responsible AI practices and enterprise governance requirements.
  • Ensure AI solutions comply with security, privacy, and regulatory requirements.
  • Apply guardrails, content controls, identity management, and access controls where appropriate.
  • Collaborate with security, compliance, and architecture teams to mitigate risks associated with AI deployments.

Requirements

Professional Experience

  • 3-6 years of software engineering or application development experience.
  • 2+ years of hands-on experience delivering AI, machine learning, or Generative AI solutions., * Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Systems, or a related technical field.
  • 3-6 years of software development experience.
  • Minimum 2 years of experience developing AI, machine learning, or Generative AI solutions.
  • Strong programming skills in one or more of the following:
  • Python
  • Java
  • C#
  • JavaScript / TypeScript
  • Experience designing and developing RESTful APIs and distributed services.
  • Experience working with cloud platforms including:
  • Microsoft Azure
  • Amazon Web Services (AWS)
  • Google Cloud Platform (GCP)
  • Solid understanding of software engineering fundamentals, including:
  • Object-oriented design
  • Design patterns
  • Automated testing
  • CI/CD pipelines
  • Application security
  • Monitoring and observability
  • Experience working within Agile software development environments.
  • Strong problem-solving, analytical, and communication skills., * Experience working with industry-leading AI platforms and models, including:
  • OpenAI
  • Azure OpenAI
  • Anthropic Claude
  • Google Gemini
  • Experience developing:
  • AI agents
  • Agent orchestration platforms
  • MCP-based integrations
  • Tool-calling frameworks
  • Multi-agent workflows
  • Experience implementing RAG architectures and enterprise knowledge search solutions.
  • Familiarity with vector databases, embeddings, semantic search, and knowledge retrieval systems.
  • Experience with prompt engineering, prompt evaluation, and AI benchmarking frameworks.
  • Experience with containerization and orchestration technologies, including:
  • Docker
  • Kubernetes
  • Cloud Run
  • Experience deploying and operating cloud-native applications.
  • Knowledge of Responsible AI, AI governance, and enterprise AI risk management practices.

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