Applied AI/ML Engineer / Data Scientist

Summary

Dice is recruiting an Applied AI/ML Engineer / Data Scientist to develop and operationalize the intelligence layer of an enterprise platform. The role combines data science, machine learning, generative AI, and software engineering to build, evaluate, deploy, and continuously improve intelligent capabilities for operational decision-making.

Responsibilities

• Design, develop, evaluate, and deploy AI/ML capabilities
• Develop analytical models for anomaly detection, asset health, forecasting, classification, and other operational use cases
• Develop generative AI and agentic capabilities using enterprise-approved foundation models and AI platforms
• Design prompts, tools, agents, workflows, and orchestration patterns
• Develop Retrieval-Augmented Generation and knowledge-retrieval solutions when appropriate
• Create rigorous evaluation frameworks for LLM and agent behavior
• Establish metrics for model accuracy, relevance, reliability, hallucination, latency, and cost
• Develop guardrails and validation mechanisms for AI-generated responses
• Collaborate with Data Engineering to define training, inference, retrieval, and feature-data requirements
• Collaborate with the Full Stack/Cloud Engineer to deploy AI services into production
• Develop prototypes rapidly while designing solutions that can transition into production
• Monitor model and agent performance and continuously improve deployed capabilities
• Communicate model behavior and analytical findings to engineers, product stakeholders, and operational subject-matter experts
• Stay current with emerging AI, agentic AI, ML, and data-science technologies and assess their applicability

Skills

• A Master's degree or Bachelor's degree with equivalent experience in Computer Science, Data Science, Engineering, Statistics, Machine Learning, or a related discipline is required
• A minimum of 4+ years of experience developing machine-learning or advanced analytics solutions is necessary
• Experience taking analytical or ML solutions from experimentation into production is also required
• Strong Python skills are required, along with experience with common ML/data-science frameworks and libraries
• Candidates must have a strong foundation in statistics, experimentation, model evaluation, and data analysis
• Experience with cloud-based data and compute environments, APIs, software-development practices, source control, and CI/CD is also needed
• A demonstrated ability to translate business or operational problems into analytical approaches is essential
• Hands-on experience developing applications using LLMs
• Experience with agentic frameworks, tool calling, MCP, or similar AI orchestration technologies
• Experience with RAG, embeddings, vector search, and knowledge-management architectures
• Experience implementing systematic LLM evaluation and guardrails
• Experience with AWS AI/ML services
• Experience with time-series analytics and anomaly detection
• Experience with industrial, energy, renewable-generation, BESS, or operational datasets
• Familiarity with MLOps and model-monitoring practices

Qualifications

Must Haves

• A Master's degree or Bachelor's degree with equivalent experience in Computer Science, Data Science, Engineering, Statistics, Machine Learning, or a related discipline is required
• A minimum of 4+ years of experience developing machine-learning or advanced analytics solutions is necessary
• Experience taking analytical or ML solutions from experimentation into production is also required
• Strong Python skills are required, along with experience with common ML/data-science frameworks and libraries
• Candidates must have a strong foundation in statistics, experimentation, model evaluation, and data analysis
• Experience with cloud-based data and compute environments, APIs, software-development practices, source control, and CI/CD is also needed
• A demonstrated ability to translate business or operational problems into analytical approaches is essential

Nice to Haves

• Hands-on experience developing applications using LLMs
• Experience with agentic frameworks, tool calling, MCP, or similar AI orchestration technologies
• Experience with RAG, embeddings, vector search, and knowledge-management architectures
• Experience implementing systematic LLM evaluation and guardrails
• Experience with AWS AI/ML services
• Experience with time-series analytics and anomaly detection
• Experience with industrial, energy, renewable-generation, BESS, or operational datasets
• Familiarity with MLOps and model-monitoring practices

Benefits

• Medical, dental, vision, life, disability, and other insurance plans
• Employee stock purchase program (ESPP)
• 401K program, with contributions typically available within 30 days of starting and a company match after 12 months of tenure
• Health Savings Account (HSA) on the HDHP plan
• SupportLinc Employee Assistance Program (EAP) with up to 8 free counseling sessions
• Corporate discount savings program and other discounts
• On-demand training program
• Access to certification prep and a library of technical and leadership courses, books, and seminars once you have 6+ months of tenure
• Certification discounts and other perks to associations that include CompTIA and IIBA
• Dedicated customer service team for Consultants to address questions around benefits and other resources
• Certified Career Coach
• Access to a full list of benefits, programs, support teams, and resources within the Welcome Packet