Principal AI/ML Software Engineer

Role Overview

The Principal AI/ML Software Engineer will build and deploy reliable, scalable production ML systems and Generative/Agentic AI features for Octave’s document-based compliance management platform.

Responsibilities

• Build and deploy Generative AI features using foundation models (AWS Bedrock, OpenAI, Anthropic Claude) and optimize latency and cost.
• Design agentic AI systems that autonomously support compliance workflows such as document review, regulatory mapping, and multi-step reasoning.
• Integrate LLM evaluation frameworks into both development and production systems.
• Own end-to-end MLOps/ModelOps: pipelines, deployment systems, monitoring, and rollback workflows.
• Implement explainability (SHAP/LIME) and build monitoring dashboards for transparency and regulatory adherence.
• Collaborate with cross-functional teams to translate business needs into ML solutions and communicate insights to stakeholders.

Requirements

• Python (5+ years) in production with: Pandas, NumPy, scikit-learn, XGBoost, TensorFlow/PyTorch, Hugging Face Transformers, FastAPI/Flask, MLflow, pytest.
• SQL advanced proficiency (complex queries, window functions, optimization).
• Strong ML & NLP foundation: supervised/unsupervised learning, deep learning, document understanding, text classification, semantic analysis.
• Generative AI/LLMs experience: GPT/Claude/Llama, prompt engineering, RAG architectures, and vector databases (Pinecone/Weaviate/Chroma).
• MLOps: end-to-end pipelines, model versioning, feature stores, drift detection, ML CI/CD, Docker.
• LLM evaluation experience (e.g., RAGAS, DeepEval), custom metrics, benchmark datasets, and human-in-the-loop validation.
• AWS: SageMaker, Bedrock, S3, Lambda, EC2, CloudWatch.
• Statistics & experimentation: A/B testing, causal inference, experimental design.
• Visualization: Tableau, Power BI, or Python visualization libraries.
• Experience: 5+ years in data science/ML engineering; 3+ years building NLP/generative AI apps and implementing MLOps in production.
• Education: BS/MS in Data Science, CS, Statistics, or related field.

Preferred Qualifications

• Agentic AI frameworks: LangGraph, LangChain, AutoGen, CrewAI.
• Knowledge of regulated industries/compliance (FDA, EMA, ISO, GxP) and compliance management systems.
• Big data/orchestration/monitoring: Spark, Databricks, Snowflake; Airflow, Kubeflow; Datadog, Prometheus.
• LLM fine-tuning, document processing libraries, multimodal AI, distributed training.
• ML governance, bias detection, model risk management, and privacy regulations (GDPR/CCPA/HIPAA).
• Agile experience (Jira), AWS ML certifications.

Key Competencies

• Strong communication to technical and non-technical audiences.
• Ability to turn POCs into production-grade solutions.
• Independent and collaborative working style in fast-paced environments.
• Ethical, explainable AI mindset suitable for regulated environments.