Job description

Role : Senior AI/ML Engineer Location : Dayton, Ohio (Hybrid) Rate: $ 65/hr to $70/hr Contract to hire Design advanced ML models across NLP, optimization, predictive modeling, and statistical learning. Own end to end MLOps pipelines: data ingestion, training, deployment, monitoring, CI/CD. Collaborate with engineering, product, and domain teams to deliver production ready AI solutions. Build and scale Knowledge Graph–driven AI systems (ontology design, graph embeddings, reasoning). Develop and fine tune LLMs for classification, summarization, RAG, and agentic workflows. What We're Looking For: PhD (preferred) or Master's in CS, AI, ML, Data Science, or related fields. Strong hands on experience with: Core ML & Stats (optimization, supervised/unsupervised learning) NLP (semantic search, embeddings, text modeling) MLOps (MLflow, Kubeflow, Airflow, Docker, CI/CD) Proficiency in Python, PyTorch/TensorFlow, HuggingFace, LangChain, and cloud platforms. Ability to translate complex ideas into scalable, real world systems. Knowledge Graphs (RDF/OWL, Neo4j, graph ML) LLMs & Transformers (fine tuning, RAG, prompt engineering) Preferred Healthcare insurance / Managed Care (MCO) experience — familiarity with claims, clinical workflows, risk models, or regulatory frameworks is a strong plus. Experience with vector databases, hybrid semantic neural architectures, or agentic AI systems. Background in explainable or responsible AI.

Requirements

Must-haves

Role : Senior AI/ML Engineer
Location : Dayton, Ohio (Hybrid)
Rate: $ 65/hr to $70/hr
Contract to hire
Design advanced ML models across NLP, optimization, predictive modeling, and statistical learning.
Own end to end MLOps pipelines: data ingestion, training, deployment, monitoring, CI/CD.
Collaborate with engineering, product, and domain teams to deliver production ready AI solutions.
Build and scale Knowledge Graph–driven AI systems (ontology design, graph embeddings, reasoning).
Develop and fine tune LLMs for classification, summarization, RAG, and agentic workflows.
Core ML & Stats (optimization, supervised/unsupervised learning)
NLP (semantic search, embeddings, text modeling)
MLOps (MLflow, Kubeflow, Airflow, Docker, CI/CD)
Proficiency in Python, PyTorch/TensorFlow, HuggingFace, LangChain, and cloud platforms.
Ability to translate complex ideas into scalable, real world systems.
Knowledge Graphs (RDF/OWL, Neo4j, graph ML)
LLMs & Transformers (fine tuning, RAG, prompt engineering)

Nice-to-haves

PhD (preferred) or Master's in CS, AI, ML, Data Science, or related fields.
Healthcare insurance / Managed Care (MCO) experience — familiarity with claims, clinical workflows, risk models, or regulatory frameworks is a strong plus.
Experience with vector databases, hybrid semantic neural architectures, or agentic AI systems.
Background in explainable or responsible AI.

Application prep

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Keywords to mirror

aimlci/cddockerpython

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