Design, build, and deploy production-ready intelligent solutions using machine learning, deep learning, generative AI, and large language models. Develop RAG pipelines, integrate AI services, manage model deployment and MLOps workflows, and apply strong software engineering practices. Collaborate with engineering, product, and data teams in a distributed environment to create scalable, reliable, responsible AI-powered products.
AI Engineer (Python, Machine Learning, Generative AI/LLMs)About Us
Founded in 2011, Modus is a global, fully remote team of world-class technologists who thrive in a collaborative, innovative environment. We’re a digital product engineering partner for forward-thinking businesses. Our global teams work side-by-side with clients to design, build, and scale custom solutions that achieve real results and lasting change, partnering with industry leaders including AWS, GitHub, and Atlassian.
We were fully remote before it was cool! Recognized as one of the Inc. 5000 Fastest Growing Private Companies for nine years and a top remote work company by FlexJobs, we have helped some of the world’s largest brands deliver powerful digital experiences.
As an award-winning Atlassian partner with a world-class team, we help organizations innovate and solve complex challenges for Fortune 500 companies and beyond, we want to hear from you.
Opportunity!We are looking for an AI Engineer to join our engineering team and design, build, and deploy intelligent solutions using modern Machine Learning, Deep Learning, and Generative AI technologies. You’ll work across the AI/ML lifecycle, from experimentation and model development to integration and production deployment, collaborating with engineering, product, and data teams to turn complex business challenges into scalable AI-powered solutions.
Requirements:- Strong proficiency in Python and experience developing production-ready AI/ML applications and services.
- Solid experience with Machine Learning, including model development, evaluation, optimization, and implementation.
- Hands-on experience building applications using Generative AI and Large Language Models (LLMs).
- Experience with Deep Learning concepts, neural networks, and modern model architectures.
- Practical experience with PyTorch and/or TensorFlow.
- Experience with Natural Language Processing (NLP), including areas such as text processing, embeddings, semantic search, classification, or generation.
- Hands-on experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines and working with vector-based retrieval.
- Experience integrating AI/ML APIs, foundation models, and third-party AI services into applications.
- Understanding of MLOps and model deployment, including versioning, monitoring, CI/CD, and production ML workflows.
- Experience with data engineering concepts, including data preparation, transformation, pipelines, and storage for AI/ML workloads.
- Strong software engineering fundamentals with a focus on maintainability, scalability, testing, and code quality.
- Ability to collaborate effectively with engineers, data teams, product teams, and other stakeholders in a distributed environment.
- Experience with LLM fine-tuning, prompt engineering, and model evaluation.
- Experience with vector databases, embeddings, and semantic retrieval systems.
- Exposure to AI agents, tool/function calling, or multi-agent architectures.
- Experience with cloud-based AI/ML platforms, containerization, and orchestration technologies.
- Experience implementing AI observability, guardrails, responsible AI, or model governance.
- Experience optimizing AI applications for performance, latency, scalability, and cost.
- Working on innovative AI-powered products with real-world scale and impact.
- Building intelligent experiences using modern Machine Learning and Generative AI technologies.
- Collaborating closely with engineering, product, and data teams to solve complex problems.
- Raising the bar for AI engineering standards, reliability, and responsible development practices.
- Exploring emerging AI technologies and continuously improving how intelligent solutions are built and delivered.
By joining our team, you’ll be part of a winning squad that plays to each other’s strengths and celebrates every success together.
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What you need to know about the Chicago Tech Scene
With vibrant neighborhoods, great food and more affordable housing than either coast, Chicago might be the most liveable major tech hub. It is the birthplace of modern commodities and futures trading, a national hub for logistics and commerce, and home to the American Medical Association and the American Bar Association. This diverse blend of industry influences has helped Chicago emerge as a major player in verticals like fintech, biotechnology, legal tech, e-commerce and logistics technology. It’s also a major hiring center for tech companies on both coasts.
Key Facts About Chicago Tech
- Number of Tech Workers: 245,800; 5.2% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: McDonald’s, John Deere, Boeing, Morningstar
- Key Industries: Artificial intelligence, biotechnology, fintech, software, logistics technology
- Funding Landscape: $2.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Pritzker Group Venture Capital, Arch Venture Partners, MATH Venture Partners, Jump Capital, Hyde Park Venture Partners
- Research Centers and Universities: Northwestern University, University of Chicago, University of Illinois Urbana-Champaign, Illinois Institute of Technology, Argonne National Laboratory, Fermi National Accelerator Laboratory
