Lead AI Engineer #3624294

Axiom-Path; Richmond (VA); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Comprehensive benefits

401(k) retirement

Generous PTO

Paid holidays

Family leave

Wellness support

Tuition reimbursement

Student loan repayment

Training & certification

Job summary

Axiom-Path in Richmond, VA seeks an experienced ML/AI engineer to design, build, train, and deploy production-grade AI solutions, with scalable pipelines on a Databricks Lakehouse. You will collaborate with product, engineering, platform, data, and business teams to deliver impactful AI outcomes.

This full-time role offers hybrid work in Richmond or remote options for eligible Eastern Time Zone states, plus comprehensive benefits, retirement savings, generous PTO, and training opportunities.

Qualifications

• 7+ years of experience in ML/AI or a similar hands-on role.
• Strong Python, Spark, Databricks, MLflow, SQL skills.
• Experience deploying and monitoring ML models in production.
• Knowledge of ML techniques: supervised/unsupervised, transformers, embeddings, LLMs.
• Experience designing reproducible ML pipelines, CI/CD, observability.
• Solid software engineering fundamentals: version control, testing, maintainable architecture.
• Ability to communicate technical concepts to non-technical stakeholders.
• Experience in agile product environments with cross-functional teams.
• Nice to have: Databricks Model Serving, Unity Catalog, Feature Store, Delta Live Tables.

Responsibilities

• Design, build, train, evaluate, and deploy ML models for predictive analytics, NLP, anomaly detection, and other 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, retrieval-augmented generation, embeddings, vector search, and related AI capabilities.
• 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 to enable future engineers and cross-functional teams.

Skills

ML/AI engineering

Python

Distributed data processing

Stakeholder communication

Agile workflow

Experimentation

CI/CD

Model evaluation

Collaboration across teams

Tools

Python

Spark

Databricks

MLflow

SQL

Delta Lake

APIs

Kubernetes

CI/CD

Job description

Be Part Of A High-Performing Team:

Join a mission-driven technology organization focused on improving the aging and long-term care experience for families, caregivers, and care seekers. This team is building modern digital solutions that bring together care options, resources, education, and human support into a more connected and accessible experience. The environment is collaborative, product-focused, and purpose-driven, with a strong emphasis on learning, inclusion, and building technology that creates meaningful real-world impact.

What\'s In Store For You:

This is a full-time opportunity available to candidates in Richmond, VA on a hybrid basis or remote candidates residing in approved Eastern Time Zone states. The role offers the opportunity to work on production-grade AI and machine learning solutions, contribute to scalable ML foundations, and partner closely with product, engineering, platform, data, and business stakeholders. Employees are offered comprehensive benefits, retirement savings options, generous paid time off, paid holidays, paid family leave, wellness support, tuition reimbursement, student loan repayment, and training/certification support. This role is not eligible for employment visa sponsorship.

How You Will Make An Impact

• 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.

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.