> Markdown version of [/jobs/ext/638344-ml-ai-engineer](https://www.wearedevelopers.com/jobs/ext/638344-ml-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML/AI Engineer - **Company:** Axiom - **Location:** Richmond, VA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Continuous Integration, Information Engineering, Data Warehousing, Python (Programming Language), Machine Learning, Performance Tuning, Search Technologies, Software Engineering, SQL Databases, Data Streaming, Supervised Learning, Retrieval-Augmented Generation, Large Language Models, Apache Spark, Deep Learning, Event Driven Architecture, Data Lakes, Kubernetes, Machine Learning Operations, Software Version Control, Unsupervised Learning, Databricks, Microservices - **Published:** June 25, 2026 - **Apply:** https://www.dice.com/job-detail/9d1b27c0-0643-4436-b461-ce5428d028de ## About the Role Are you an experienced ML/AI engineering professional ready to build production-grade intelligent systems? * 7+ years of experience in machine learning, applied AI, machine learning engineering, or a similar hands-on technical role. * Strong hands-on expertise with Python, Spark, Databricks, MLflow, SQL, and large-scale distributed datasets. * Experience building, deploying, and monitoring machine learning models in production environments. * Strong understanding of modern ML techniques, including supervised learning, unsupervised learning, deep learning, transformers, embeddings, vector stores, and LLM-based systems. * Experience designing reproducible ML pipelines, CI/CD workflows, model deployment patterns, and observability practices. * Solid software engineering foundation, including version control, testing, modular architecture, maintainability, and production reliability. * Ability to communicate technical concepts clearly to non-technical stakeholders and influence technical direction across teams. * Experience working in agile product environments with product managers, engineers, data teams, and business partners. * Nice to have: Databricks Model Serving, Unity Catalog, Feature Store, Delta Live Tables, RAG systems, LLM fine-tuning, model distillation, AWS, Azure, Kubernetes, containers, real-time ML, streaming data, or event-driven architectures. * Strong curiosity, collaboration skills, ownership mindset, and ability to work through ambiguity with incomplete data or evolving requirements. ## Description * Design, build, train, evaluate, and deploy machine learning models for predictive analytics, classification, NLP, anomaly detection, generative AI, and other applied AI use cases. * Develop production-ready AI/ML solutions on a Databricks Lakehouse platform using Python, Spark, MLflow, Delta Lake, and related tools. * Build scalable feature pipelines, training workflows, validation processes, and model refresh cycles that are automated, reproducible, and reliable. * Own the end-to-end ML lifecycle, including experimentation, model registry, deployment, monitoring, drift detection, model performance tracking, and ongoing optimization. * Design modular ML architectures that integrate with APIs, data warehouses, microservices, and downstream applications. * Develop LLM-powered applications, prompt engineering strategies, retrieval-augmented generation systems, embeddings, vector search, and related AI capabilities where appropriate. * Partner with data engineering, product, platform engineering, and business stakeholders to turn ambiguous business opportunities into measurable AI/ML outcomes. * Support experimentation through A/B testing, offline and online evaluation frameworks, statistical validation, and clear communication of results. * Document models, systems, decisions, and workflows in a way that enables future engineers and cross-functional teams to adopt and maintain solutions. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [Enabling intelligent logistics automation: home-grown Industrial IoT platform at Austrian Post](https://www.wearedevelopers.com/videos/2018-enabling-intelligent-logistics-automation-home-grown-industrial-iot-platform-at-austrian-post) - [The Data Mesh as the end of the Datalake as we know it](https://www.wearedevelopers.com/videos/156-the-data-mesh-as-the-end-of-the-datalake-as-we-know-it) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)