[Hiring] Senior AI/Machine Learning Engineer @Intellivo

Role Description

This is a builder’s role, not a research role. You will write the Python that puts AI models to work on real production problems: reading messy documents and email, resolving entities across systems, enriching records, scoring likelihood, and surfacing signals that were previously invisible.

We are looking for an engineer with good working knowledge of transformer architecture and practical experience with foundation models on both sides of the market: open-source models you can host and run, and commercial models you consume through an API. You do not need to have trained one from scratch. You do need to be comfortable calling them, prompting them well, handling their output, and building reliable services around them.

The role also spans traditional machine learning. We have a large and interesting data set, and part of the job is finding the modeling opportunities hiding inside it that translate into better recovery outcomes: classification, matching, scoring, and prediction. You will also help with synthetic data approaches where real data is limited or contractually restricted. This is a remote position; candidates in or near Memphis, TN are preferred.

Core Responsibilities

• Write clean, production-quality Python that integrates foundation models into automated pipelines and services.
• Work with both open-source and commercial foundation models, including prompt design, tool calling, structured output, error and retry handling, and evaluating which model fits a given workload on accuracy, latency, and cost.
• Uncover and shape modeling opportunities in our data that lead to stronger recovery outcomes, then build them: classification, entity matching, ranking, and propensity or likelihood scoring.
• Design, train, evaluate, and deploy machine learning models using standard modeling and automated machine learning platforms.
• Build retrieval and multi-step model workflows using orchestration frameworks, including state handling and guardrails.
• Help develop synthetic data approaches where real data is sparse, sensitive, or contractually restricted, including generation strategy and validating that the synthetic data actually improves model performance.
• Support the machine learning operations layer: training and inference pipelines, model versioning, deployment automation, and monitoring for drift and performance.
• Integrate models into production applications and workflows through APIs and services.
• Build practical evaluation into everything you ship: test sets, before-and-after comparisons, human review where it matters, and honest reporting of failure modes.
• Optimize models and services for performance, scalability, and cost, including inference and token consumption.
• Spot opportunities in the data while organizing chaos and cutting through noise.
• Follow responsible AI and data handling practice: PHI protection, access controls, model documentation, and traceability of what a model was trained on.

Qualifications

• 5+ years in machine learning, data science, or data engineering, including experience putting models into production use.
• Strong proficiency in Python and SQL.
• Good working knowledge of transformer architecture and how modern foundation models behave.
• Practical experience with both open-source and commercial foundation models, such as Llama, Mistral, or Qwen alongside Anthropic Claude or OpenAI.
• Experience with supervised learning tooling such as SageMaker, H2O, scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Experience with LangChain and LangGraph, or a comparable framework for multi-step model workflows.
• Exposure to synthetic data generation approaches and how to validate them.
• Working knowledge of Microsoft Azure for deploying and operating machine learning workloads.
• Familiarity with model evaluation, vector stores, and retrieval-augmented generation patterns.
• Knowledge of healthcare and insurance data is strongly preferred.

Requirements

• Comfort across both traditional machine learning and generative AI.
• Solid engineering habits: version control, testing, code review, reproducibility, and documentation.
• Cost awareness in model selection and design, including token and inference spend.
• Analytical rigor and critical thinking when facing ambiguous, messy, real-world data.
• Clear communication of model behavior, limitations, and results to both technical and business audiences.
• Self-starter with a track record of achievement who will roll up sleeves to tackle hard projects.

Education

• B.S./B.A. required in Computer Science, Statistics, Mathematics, Engineering, a related technical field, or equivalent experience.

License/Certification

• Azure AI or data science certification (e.g., AI-102 or DP-100) preferred.
• AWS Machine Learning certification a plus.

Preferred

• Deeper experience with healthcare, insurance, or claims data in a regulated, high-compliance environment.
• Experience with entity resolution, record linkage, or fuzzy matching.
• Experience with document intelligence, OCR, or information extraction from unstructured text and email.
• Contributions to open-source machine learning projects.
• Located in or near Memphis, TN.

Benefits

• Comprehensive medical, dental, and vision insurance.
• 401(k) retirement savings plan with employer match.
• Paid time off and paid holidays.
• Company-paid life insurance and short-term and long-term disability coverage.
• Employee Assistance Program with counseling, financial coaching, legal resources, career coaching, and wellness support.
• Health Savings Account with company contributions for eligible employees.
• Wellness, healthcare advocacy, and pet benefits.
• A high-performing, collaborative culture built on ownership, accountability, continuous improvement, and meaningful impact.