Jones Grove IT Recruiting
AI & ML Solutions Engineer
Jones Grove IT Recruiting, Charlotte, North Carolina, United States, 28245
AI & ML Solutions Engineer
Full Time - Permanent Position
Charlotte, NC (Hybrid)
About the Role
We are looking for a hands-on, consultative technologist to lead the design, development, and deployment of AI/ML and GenAI solutions across the business. This is a high-impact role for someone who thrives at the intersection of engineering, data science, and business outcomes.
As the AI & ML Solutions Engineer, you'll be the team's "Swiss Army knife"-capable of building end-to-end solutions: from shaping business use cases to architecting pipelines, deploying models, and prototyping GenAI applications. You'll work directly with internal stakeholders to deliver measurable value, helping us automate, predict, and optimize across operations and supply chain.
We're not looking for a domain expert in one area-we're looking for a strong generalist who can go deep where needed. If you're driven to experiment, move fast, and make things real, we want to talk.
What You'll Do Own the AI lifecycle - Build, deploy, and maintain ML and GenAI solutions using Databricks, Azure, MLflow, and Snowflake. Experiment & deliver quickly - Run rapid proof-of-concepts to validate ideas, then scale what works into production. Automate for impact - Develop Python/R-based workflows, ML pipelines, and API endpoints to automate business processes. Lead solution design - Translate ambiguous business challenges into technical solutions, including MLOps infrastructure, model selection, and GenAI frameworks. Collaborate cross-functionally - Work closely with data engineers, architects, and business stakeholders to align solutions with strategic priorities. Enhance team capability - Share best practices and contribute to a culture of experimentation, continuous learning, and delivery. The Tech Stack
Cloud & Data Platforms: Azure, Snowflake, Databricks Languages & Tools: Python (Pandas, PySpark, scikit-learn, FastAPI), R MLOps: MLflow, CI/CD (GitHub Actions), Docker, Kubernetes (AKS) GenAI: OpenAI, LangChain, LlamaIndex, vector databases (FAISS, Pinecone, Weaviate), RAG pipelines Visualization/Apps: Power BI, Power Automate, Teams-integrated tools What We're Looking For
Must-Haves:
3-5+ years of experience in data science, machine learning engineering, or full-stack AI solutioning Experience with end-to-end ML pipeline delivery-from data prep and feature engineering to deployment and monitoring Strong Python skills and familiarity with Databricks, Snowflake, and cloud-native workflows Exposure to GenAI tools and frameworks, including building or experimenting with LLM-based applications including OpenAI (GPT-4), Anthropic (Claude), Meta (LLaMA 2/3), Mistral, Google Gemini, and Cohere Understanding of MLOps practices: model promotion, versioning, retraining, and feature stores Ability to work with stakeholders, frame business problems, and deliver outcomes-not just models Comfortable failing fast and iterating quickly based on feedback Nice to Haves:
Consulting background or experience working directly with non-technical stakeholders Experience in manufacturing, supply chain, or industrial domains Familiarity with infrastructure-as-code and Git-based workflows Experience integrating AI/ML outputs into user-facing tools or internal workflows (e.g., Teams bots, dashboards)
Full Time - Permanent Position
Charlotte, NC (Hybrid)
About the Role
We are looking for a hands-on, consultative technologist to lead the design, development, and deployment of AI/ML and GenAI solutions across the business. This is a high-impact role for someone who thrives at the intersection of engineering, data science, and business outcomes.
As the AI & ML Solutions Engineer, you'll be the team's "Swiss Army knife"-capable of building end-to-end solutions: from shaping business use cases to architecting pipelines, deploying models, and prototyping GenAI applications. You'll work directly with internal stakeholders to deliver measurable value, helping us automate, predict, and optimize across operations and supply chain.
We're not looking for a domain expert in one area-we're looking for a strong generalist who can go deep where needed. If you're driven to experiment, move fast, and make things real, we want to talk.
What You'll Do Own the AI lifecycle - Build, deploy, and maintain ML and GenAI solutions using Databricks, Azure, MLflow, and Snowflake. Experiment & deliver quickly - Run rapid proof-of-concepts to validate ideas, then scale what works into production. Automate for impact - Develop Python/R-based workflows, ML pipelines, and API endpoints to automate business processes. Lead solution design - Translate ambiguous business challenges into technical solutions, including MLOps infrastructure, model selection, and GenAI frameworks. Collaborate cross-functionally - Work closely with data engineers, architects, and business stakeholders to align solutions with strategic priorities. Enhance team capability - Share best practices and contribute to a culture of experimentation, continuous learning, and delivery. The Tech Stack
Cloud & Data Platforms: Azure, Snowflake, Databricks Languages & Tools: Python (Pandas, PySpark, scikit-learn, FastAPI), R MLOps: MLflow, CI/CD (GitHub Actions), Docker, Kubernetes (AKS) GenAI: OpenAI, LangChain, LlamaIndex, vector databases (FAISS, Pinecone, Weaviate), RAG pipelines Visualization/Apps: Power BI, Power Automate, Teams-integrated tools What We're Looking For
Must-Haves:
3-5+ years of experience in data science, machine learning engineering, or full-stack AI solutioning Experience with end-to-end ML pipeline delivery-from data prep and feature engineering to deployment and monitoring Strong Python skills and familiarity with Databricks, Snowflake, and cloud-native workflows Exposure to GenAI tools and frameworks, including building or experimenting with LLM-based applications including OpenAI (GPT-4), Anthropic (Claude), Meta (LLaMA 2/3), Mistral, Google Gemini, and Cohere Understanding of MLOps practices: model promotion, versioning, retraining, and feature stores Ability to work with stakeholders, frame business problems, and deliver outcomes-not just models Comfortable failing fast and iterating quickly based on feedback Nice to Haves:
Consulting background or experience working directly with non-technical stakeholders Experience in manufacturing, supply chain, or industrial domains Familiarity with infrastructure-as-code and Git-based workflows Experience integrating AI/ML outputs into user-facing tools or internal workflows (e.g., Teams bots, dashboards)