Principal AI/ML Software Engineer

Geomatik; Houston (TX); Híbrido; USD 180.000 - 240.000; Jornada completa

Descripción de la vacante

Octave is seeking an AI/ML Engineer to design, deploy, and operate production-grade ML systems for a document-based compliance platform. You will develop Generative AI features, agentic workflows, and robust MLOps with monitoring, explainability, and governance in mind.

You will collaborate with cross-functional teams, translate complex business needs into ML solutions, and communicate insights to stakeholders while advancing the AI roadmap for regulated environments.

Formación

• 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, CS, Statistics, or related field.
• Track record of deploying ML systems processing large-scale datasets with proper monitoring and governance.

Responsabilidades

• Build and deploy Generative AI features using foundation models and inference pipelines.
• Design agentic AI systems that autonomously handle compliance workflows and document review.
• 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 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.

Conocimientos

Python

SQL

ML/NLP

Generative AI

LLMs

MLOps

AWS

Pandas/NumPy

Transformers

Docker

FastAPI/Flask

Educación

Bachelor's or Master's in Data Science/CS/Statistics

Herramientas

TensorFlow/PyTorch

Hugging Face Transformers

MLflow

Pinecone

Weaviate

Chroma

SageMaker

Bedrock

Docker

Descripción del empleo

Job Location (Short): Houston, Texas-USA | Madison, Alabama-USA | Roanoke, Virginia-USA

Workplace Type: Remote

Responsibilities

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
• 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
• 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

About Octave

Octave provides mission-critical software that empowers organizations to make informed decisions across every stage of the asset lifecycle — Design, Build,Operateand Protect — where performance, safety, and reliability are non-negotiable and failure is not an option.

Turning complex operational data into actionable intelligence, Octave connectsexpertise, real-worldconditionsand enterprise-scale insight to improve performance,resilienceand incident response where it matters most.

Octave has approximately 7,200 employees in 45 countries. Learn more at octave.com and follow us on LinkedIn .

Why work for Octave?

All in. Always forward. That's the way we do things around here. We put trust in our people because we believe it's the best way to unleash potential, bring ideas to life, and keep moving ahead. And it's why we're committed to creating an environment that's truly supportive, providing you with the resources you need to support your ambitions, no matter who you are or where you are in the world.

Everyone is welcome

At Octave, we believe that diverse and inclusive teams are critical to the success of our people and our business. Here, everyone is welcome. As an inclusive workplace, we don't discriminate. In fact, we embrace differences and are fully committed to creating equal opportunities, an inclusive environment, and fairness for all.

Respect is the cornerstone of how we operate, so speak up and be yourself. You're valued here.