Applied AI/ML Engineer

Required Skills & Experience

Bachelor’s degree with equivalent experience in Computer Science, Data Science, Engineering, Statistics, Machine Learning or a related discipline. \n4+ years of experience developing machine-learning or advanced analytics solutions. \nStrong Python skills. \nExperience with common ML/data-science frameworks and libraries. \nExperience taking analytical or ML solutions from experimentation into production. \nStrong foundation in statistics, experimentation, model evaluation and data analysis. \nExperience working with cloud-based data and compute environments. \nExperience with APIs, software-development practices, source control and CI/CD. \nDemonstrated ability to translate business or operational problems into analytical approaches.

Job Description

Insight Global is seeking an Applied AI/ML Engineer- Data Scientist to develop and operationalize the intelligence layer of off-prem platform. This role will combine data science, machine learning, generative AI and software engineering to create intelligent capabilities that help users understand operational information, diagnose issues, identify anomalies, retrieve knowledge, and make better decisions. Unlike a traditional research-focused Data Scientist position, this role emphasizes taking AI capabilities from experimentation through production deployment and evaluation.\nDesign, develop, evaluate and deploy AI/ML capabilities \nDevelop analytical models for anomaly detection, asset health, forecasting, classification and other operational use cases. \nDevelop generative AI and agentic capabilities using enterprise-approved foundation models and AI platforms. \nDesign prompts, tools, agents, workflows and orchestration patterns. \nDevelop Retrieval-Augmented Generation (RAG) and knowledge-retrieval solutions when appropriate. \nCreate rigorous evaluation frameworks for LLM and agent behavior. \nEstablish metrics for model accuracy, relevance, reliability, hallucination, latency and cost. \nDevelop guardrails and validation mechanisms for AI-generated responses. \nCollaborate with Data Engineering to define training, inference, retrieval and feature-data requirements. \nCollaborate with the Full Stack/Cloud Engineer to deploy AI services into production. \nDevelop prototypes rapidly while designing solutions that can transition into production. \nMonitor model and agent performance and continuously improve deployed capabilities. \nCommunicate model behavior and analytical findings to engineers, product stakeholders and operational subject-matter experts. \nStay current with emerging AI, agentic AI, ML and data-science technologies and assess their applicability.\n\n$65-$75