Applied AI Engineer

Insight Global is seeking an

Applied AI/ML Engineer (Data Scientist)

to help build the intelligence layer of a mission-critical platform supporting renewable energy generation, electrical transmission, and grid operations.

This individual will develop and deploy AI, machine learning, and advanced analytics solutions that help engineers, operators, and business stakeholders better understand system performance, identify anomalies, improve asset reliability, accelerate troubleshooting, and optimize decision-making across renewable energy assets and transmission infrastructure.

This is an applied engineering role focused on delivering production-ready AI capabilities rather than academic research. The ideal candidate enjoys building end-to-end solutions and collaborating with engineering teams to operationalize AI technologies that create measurable impact in real-world utility and energy environments.

Responsibilities

• Design, develop, evaluate, and deploy AI and machine learning solutions supporting renewable energy and electric transmission operations.
• Develop predictive models and advanced analytics for asset health monitoring, anomaly detection, forecasting, reliability analysis, fault identification, and operational optimization.
• Build generative AI and agent-based solutions using enterprise-approved large language models (LLMs) and AI platforms.
• Develop AI-powered systems that assist engineers and operators with troubleshooting, knowledge retrieval, incident analysis, and operational decision support.
• Design prompts, agents, tools, workflows, and orchestration patterns to improve workforce productivity and access to institutional knowledge.
• Develop Retrieval-Augmented Generation (RAG) solutions that leverage engineering documentation, operating procedures, technical drawings, maintenance records, and operational data.
• Create rigorous evaluation frameworks to measure model and agent performance.
• Define and monitor metrics related to accuracy, relevance, reliability, hallucination rates, latency, business impact, and operational cost.
• Implement AI governance controls, validation mechanisms, and guardrails to ensure trustworthy and compliant AI outputs.
• Partner with Data Engineering teams to define data ingestion, feature engineering, training, retrieval, and inference requirements.
• Collaborate with Cloud and Software Engineering teams to deploy scalable AI services into production environments.
• Rapidly prototype innovative AI solutions while maintaining a clear path toward enterprise deployment.
• Continuously monitor model performance and drive optimization efforts post-deployment.
• Communicate analytical findings and AI-driven insights to engineers, operations teams, product stakeholders, and leadership.
• Stay current on advancements in machine learning, generative AI, agentic AI, energy analytics, and utility technology.

Required Qualifications

• Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, Machine Learning, Mathematics, or a related discipline, or equivalent professional experience.
• 4+ years of experience developing machine learning, artificial intelligence, or advanced analytics solutions.
• Strong programming experience with Python.
• Experience with machine learning and data science frameworks such as Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy, or similar technologies.
• Experience deploying AI/ML solutions into production environments.
• Strong foundation in statistics, predictive modeling, experimentation, model evaluation, and data analysis.
• Experience working in cloud-based data and compute environments such as AWS, Azure, or Google Cloud Platform.
• Experience developing APIs and working within modern software development practices, source control, and CI/CD pipelines.
• Demonstrated ability to translate complex business, engineering, or operational challenges into analytical solutions.
• Strong communication and stakeholder management skills.

Preferred Qualifications

• Experience with Large Language Models (LLMs), Generative AI, Agentic AI, and RAG architectures.
• Experience supporting utility, renewable energy, power generation, electrical transmission, grid modernization, energy trading, or industrial operations environments.
• Familiarity with operational technology (OT), SCADA systems, telemetry data, asset management systems, or utility operations.
• Experience analyzing time-series, sensor, equipment, or operational data.
• Knowledge of MLOps, AI governance, model monitoring, and responsible AI practices.
• Experience building intelligent solutions that support reliability, maintenance, outage management, asset performance, or grid operations.