Principal Software Developer

Job Location (Short): Houston, Texas-USA | Madison, Alabama-USA Workplace Type: Remote Req Id: 2909 Position Overview 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. Key Responsibilities 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 Education / Qualifications Technical Skills 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 Experience & Education 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 What You'll Build 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 J-18808-Ljbffr