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# Principal AI/ML Software Engineer

- **Company:** OCTAVE LLC
- **Location:** Madison, AL, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Airflow, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, JIRA, Big Data, Continuous Integration, Database Queries, Monitoring of Systems, Python (Programming Language), Machine Learning, NumPy, Performance Tuning, Power BI, Tensorflow, Prometheus, SQL Databases, Tableau (Software), Datadog, Pytorch, Flask (Web Framework), Large Language Models, Snowflake, Prompt Engineering, Apache Spark, Deep Learning, Generative AI, Fastapi, Pandas, Build Management, Pytest, Scikit Learn, Kubernetes, Information Technology, HuggingFace, Data Analytics, Xgboost, Data Management, Machine Learning Operations, Virtual Agents, Functional Programming, Cloudwatch, Document Classification, GPT, Software Version Control, GXP, Docker, Unsupervised Learning, Databricks
- **Published:** July 28, 2026
- **Apply:** https://www.dice.com/job-detail/72f8920a-d183-4990-805f-dfd7c2553627

## About the Role

Python (5+ years): Production-level experience with Pandas, NumPy, scikit-learn, XGBoost, TensorFlow/PyTorch, Hugging Face Transformers, FastAPI/Flask, MLflow, and pytest
SQL: Advanced proficiency with complex queries, window functions, and optimization
Machine Learning & NLP: Strong foundation in supervised/unsupervised learning, deep learning, document understanding, text classification, and semantic analysis
Generative AI & LLMs: Hands-on experience with foundation models (GPT, Claude, Llama), prompt engineering, RAG architectures, and vector databases (Pinecone, Weaviate, Chroma)
MLOps & ModelOps: End-to-end experience with ML pipelines, model versioning, feature stores, drift detection, CI/CD for ML, and Docker containerization
LLM Evaluation: Experience with evaluation frameworks (RAGAS, DeepEval), custom metrics, benchmark datasets, and human-in-the-loop validation
Cloud & AWS: Experience with AWS services including SageMaker, Bedrock, S3, Lambda, EC2, and CloudWatch
Statistics & Experimentation: Strong foundation in statistics, A/B testing, causal inference, and experimental design
Visualization: Proficiency with Tableau, Power BI, or Python visualization libraries, 5+ years in data science, ML engineering, or related roles
3+ years building NLP/generative AI applications and implementing MLOps in production
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or related field
Track record of deploying ML systems processing large-scale datasets with proper monitoring and governance

Preferred Qualifications
Experience with agentic AI frameworks (LangGraph, LangChain, AutoGen, CrewAI) ?
Knowledge of Life Sciences/regulated industries (FDA, EMA, ISO, GxP) and compliance management systems
Familiarity with big data tools (Spark, Databricks, Snowflake), orchestration (Airflow, Kubeflow), and monitoring tools (Datadog, Prometheus)
Experience with LLM fine-tuning, document processing libraries, multi-modal AI, or distributed training
Understanding of ML governance, bias detection, model risk management, and data privacy regulations (GDPR, CCPA, HIPAA)
Experience working in agile environments with Jira
AWS ML certifications or similar credentials

Key Competencies
Strong communication skills explaining complex models to technical and nontechnical audiences
Ability to work independently and collaboratively in fast-paced environments
Proven ability to convert POCs into production-grade solutions
Understanding of ethical AI and building trustworthy, explainable systems for regulated environments

## Description

We are seeking a motivated AI/ML Engineer to build reliable, scalable systems and Generative AI and Agentic AI features, and build and deploy data-driven solutions for our document-based compliance management platform. This role requires a technical expert who can develop, deploy, and maintain ML systems in production environments., Build and deploy Generative AI features using foundation models (AWS Bedrock, OpenAI, Anthropic Claude) and inference pipelines with optimization of latency and cost
Design agentic AI systems that autonomously handle compliance workflows, document review, regulatory mapping, and multi-step reasoning tasks
Integrate comprehensive LLM evaluation frameworks with development and production systems
Build and operate end-to-end MLOps pipelines, deployment systems, monitoring, and rollbacks workflows
Implement explainability frameworks (SHAP/LIME) and monitoring dashboards ensuring transparency and regulatory adherence
Collaborate with cross-functional teams to translate business needs into ML solutions and communicate insights to stakeholders, LLM evaluation frameworks ensuring 95%+ accuracy for compliance-critical features
Prompts for LLMs to achieve specific, high-quality outcomes
Agentic AI systems autonomously handling document review and compliance workflows
GenAI document understanding features processing millions of regulatory documents
Predictive models identifying compliance risks before they occur
Real-time semantic search and explainable ML systems meeting regulatory requirements
Production MLOps pipelines supporting dozens of models with automated monitoring and retraining

Growth Opportunities
Drive adoption of emerging AI technologies and establish best practices
Mentor ML engineers
Shape AI/ML roadmap and establish center of excellence for compliance AI
Collaborate with product leadership on long-term vision for AI-powered compliance


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