• Design and deploy a locally hosted LLM-powered agent for autonomous payment failure analysis • Build internal LLM infrastructure with no external API dependency for core workflows • Develop structured pipelines for root cause identification across transaction failures • Automate Level 1 incident investigations • Generate standardized root cause analysis (RCA) reports • Optimize model performance to reduce Mean Time to Resolution (MTTR) • Develop scalable training and inference pipelines • Create reusable model components adopted across multiple use cases • Reduce time-to-deploy new AI applications • Decrease reliance on external AI APIs through internal model development • Implement monitoring systems for latency, drift, and model performance • Deploy at least two additional AI use cases (e.g., chatbot, automated reporting, issue clustering) • Ensure ≥99.9% production uptime • Maintain inference latency within defined thresholds • Establish retraining cadence and continuous performance evaluation • Deliver measurable efficiency improvements in operational workflows • Implement version-controlled datasets and model versioning • Define evaluation benchmarks (precision, recall, accuracy thresholds) • Implement automated drift detection • Document model architecture and training processes • Ensure zero preventable production-critical failures due to model design • Ensure personal information of customers, employees, and other individuals processed and protected in line with applicable data privacy policies.

We are looking for a Master’s or PhD student to work on fine-tuning large language models (LLMs) for domain-specific tasks. The goal is to take an existing pretrained model (e.g., Meta AI’s LLaMA-class models or similar) and specialize it for a narrow, high-value use case using efficient fine-tuning techniques. This is a hands-on applied project designed for someone who wants real-world experience deploying and optimising LLM systems. Help drive the next wave of applied AI by demonstrating how fine-tuned LLMs can unlock advanced, real-world use cases beyond general-purpose foundation models. Contribute to building specialised AI systems that deliver improved accuracy, efficiency, and control compared to out-of-the-box models. Bridge the gap between academic knowledge and real-world application by applying fine-tuning techniques to solve concrete business problems.

Company Overview: For more than 50 years, NISC has worked to develop technology solutions for our customers, who we call our “Members”. Those Members are comprised primarily of 960+ energy (utility) and communication (broadband) organizations across North America. Our mission is to deliver technology solutions and services that are Member-focused, quality driven and valued priced. Our Members have over 18 million end customers (residential and businesses who receive power, internet, television and/or telephone services) that our enterprise software solution enables our Member's employees to compete effectively in the industry, while excelling in providing customer service to their end customers. NISC exists to serve our Members and help them serve their communities through our innovative software products, services and outstanding customer support. We are an AI-forward company committed to being a technology leader in our industry. NISC has been ranked in ComputerWorld’s Best Places to Work for 23 years, and we are looking for qualified individuals to join our team. Position Overview: We are looking for a high-agency, AI-native Software Engineer who loves building real-world AI systems that make a measurable difference. This is not a research role. This is not a “play with models in a lab” role. This is a builder role. You will design, build, ship, and continuously improve production-grade AI applications that are embedded directly into mission-critical software used by utilities and broadband providers across North America. Your work will power customer service interactions, operational workflows, and intelligent automation at scale. We are looking for someone who: Gets excited about taking AI from idea → prototype → production → scale Loves experimenting with new models, frameworks, and agentic patterns Has a “there’s a way” mindset when solving hard technical problems Believes AI should drive tangible business impact, not just technical novelty Thrives in ambiguity and moves fast while maintaining engineering discipline At NISC, AI is not a side initiative — it is a core strategic priority. You will help shape how AI transforms the way our Members serve their communities. If you’ve built and deployed AI systems that real users depend on — and you want to do it in an organization that values ownership, innovation, and meaningful impact — we encourage you to apply. Essential Functions: Design & Build Production AI Systems: Architect, develop, and deploy AI-powered features and applications using LLMs, RAG pipelines, agentic workflows, and modern ML techniques. Own Outcomes, Not Just Code: Take ideas from concept to production. Prototype quickly, validate with users, iterate based on feedback, and optimize for reliability and performance. LLM & Agent Integration: Integrate foundation models, fine-tuned models, and multi-agent systems into enterprise-grade applications in secure, scalable environments. Data & Retrieval Engineering: Design and optimize data ingestion pipelines, embeddings workflows, and vector database implementations that support high-quality AI responses. Performance & Scalability: Deploy AI systems that are resilient, observable, and cost-efficient in production environments. Mission Alignment: Ensure solutions are practical, intuitive, and aligned with the operational realities of cooperatives, utilities, and broadband providers. Continuous Innovation: Actively explore new AI capabilities and rapidly evaluate how they can improve our products and development practices. Collaborative Execution: Partner with product, architecture, and engineering teams to embed AI capabilities across our enterprise platform. Follow all established software development methodologies and procedures. Other duties as assigned Desired Experience & Skills: 8-10+ years experience in software development, preferably with at least 3+ years in AI/ML software development. Demonstrated experience shipping AI solutions used by real customers — not just prototypes or academic projects Experience integrating LLMs into applications (prompt engineering, evaluation, fine-tuning, API orchestration) Experience designing RAG pipelines, embeddings workflows, and working with vector databases Experience deploying AI systems in cloud environments (AWS, Azure, GCP) Technical Expertise: Strong Python proficiency and familiarity with ML frameworks (PyTorch, TensorFlow, or equivalent) Experience building scalable APIs and backend services Understanding of agentic architectures, orchestration frameworks, or multi-agent systems Experience optimizing AI performance, cost, and latency in production Familiarity with observability, evaluation frameworks, and model monitoring Strong Python proficiency and familiarity with ML frameworks (PyTorch, TensorFlow, or equivalent) Experience building scalable APIs and backend services Understanding of agentic architectures, orchestration frameworks, or multi-agent systems Experience optimizing AI performance, cost, and latency in production Familiarity with observability, evaluation frameworks, and model monitoring Mindset & Attributes (Critical for Success) Builder mentality — you love shipping High ownership — you see problems and move toward them Curiosity about emerging AI capabilities and how to apply them pragmatically Bias toward action and experimentation Comfort operating in evolving AI landscapes Ability to balance speed with engineering discipline Strong communicator who can translate AI capabilities into business value Builder mentality — you love shipping High ownership — you see problems and move toward them Curiosity about emerging AI capabilities and how to apply them pragmatically Bias toward action and experimentation Comfort operating in evolving AI landscapes Ability to balance speed with engineering discipline Strong communicator who can translate AI capabilities into business value Bonus Experience Experience in utilities, broadband, cooperatives, or enterprise SaaS Knowledge of AI safety, evaluation strategies, or explainability techniques Full-stack experience or ability to deliver end-to-end AI features Portfolio, GitHub, or case studies demonstrating deployed AI systems Experience in utilities, broadband, cooperatives, or enterprise SaaS Knowledge of AI safety, evaluation strategies, or explainability techniques Full-stack experience or ability to deliver end-to-end AI features Portfolio, GitHub, or case studies demonstrating deployed AI systems Experience working with utilities, broadband providers, or cooperatives, preferred. Commitment to NISC’s Statement of Shared Values NISC’s Shared Values & Competencies: We’re a cooperative, which means we’re owned by the Members we serve. It also means that our focus is on taking care of our Members and our employees, rather than having a big bottom line. Quality service and innovative technology starts with happy and dedicated employees. Join our team and learn for yourself what sets NISC apart. Integrity – We are committed to doing the right thing – always. Relationships – We are committed to building and preserving lasting relationships. Innovation – We promote the spirit of creativity and champion new ideas. Teamwork – We exemplify the cooperative spirit by working together. Empowerment – We believe individuals have the power to make a difference. Personal Development – We believe the free exchange of knowledge and information is absolutely necessary to the success of each individual and the organization. Work Schedule: Hybrid from one of our office locations: Cedar Rapids, IA Lake Saint Louis, MO Mandan, ND Cedar Rapids, IA Lake Saint Louis, MO Mandan, ND Hybrid Schedule: Minimum of working 3 day per week out of an office location and ability to work up to all 5 days a week from an office location. Required Days from an Office Location: Tuesday and Wednesday - the third required day will be up to the candidate and their supervisor to choose. Required Days from an Office Location: Tuesday and Wednesday - the third required day will be up to the candidate and their supervisor to choose. Virtual Office Location : Fully remote candidates will be considered for candidates who have a background in providing AI/ML solutions that work in real world applications, not just conceptual understanding or research based projects. Benefits: Medical, Dental and Vision Insurance. Health Savings Account (HSA) with $100 monthly contributions from NISC. Like to walk? Improve your overall wellness knowledge? Ability to earn up to $800 additional dollars into your HSA each year through our Wellness Rewards program. Like to walk? Improve your overall wellness knowledge? Ability to earn up to $800 additional dollars into your HSA each year through our Wellness Rewards program. Dependent Care Flexible Spending Account (FSA) thru Paylocity. Fully covered life insurance up to x3 annual base salary. Fully covered short- and long-term disability. 401(k), traditional or Roth, with employee match up to 6% and employer 4% salary base contributions. PTO accrual levels dependent on years of service, 120 Life Leave Event hours, and 9 paid holidays. $2,500 Interest-FREE technology loan program. $25,000 employee educational assistance program. Volunteer, Wellness, Family Events and other employee fun supplied by our committees. Employee Assistance Program; assisting employees and dependents with virtually any life event Benevolence Committee to support employees with financial hardships like unexpected medical bills, funerals and other unfortunate hardships. Desired Education: Bachelor’s Degree in a Computer Science, Computer Engineering, or a related field and/or equivalent experience. Master's Degree in Artificial Intelligence or Machine Learning, preferred. Minimum Physical Requirements: The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this position. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Employees must be able to see, speak, and hear, to operate computer keyboards or office equipment, and are required to stand, walk, and sit. Disclaimer: Management may modify this job description by assigning or reassigning duties and responsibilities at any time.

Strategic Leadership & Opportunity Development Drive top-of-funnel opportunity creation through two parallel tracks: engaging C-level stakeholders with generative AI demonstrations (Amazon Q, Amazon Bedrock) and identifying data modernization needs for Lakehouse transformations. Lead the design and architecture of dual solution portfolios: Generative AI Solutions: Amazon Bedrock implementations, Amazon Q deployments, QuickSight with Q capabilities, RAG architectures, and custom LLM solutions. Data Modernization: Enterprise Lakehouse architectures using AWS Glue, SageMaker Unified Studio, Databricks on AWS, and Snowflake on AWS. Act as the trusted advisor, positioning generative AI as the transformational vision while grounding delivery in robust data platform modernization. Develop compelling business cases that connect AI aspirations with practical data foundation requirements, demonstrating ROI across both portfolios. Stay current with advancements in generative AI (foundation models, LLMs) and modern data architectures (Lakehouse patterns, data mesh, unified analytics). Contribute to Rackspace's intellectual property through reference architectures covering both generative AI implementations and Lakehouse design patterns. Mentor and provide leadership to Solution Architects by guiding technical development and fostering skill growth across both generative AI and data modernization solution areas. Customer Engagement & Solution Delivery Serve as the primary technical lead orchestrating both generative AI discussions and data modernization programs for strategic accounts. Build strategic relationships using two engagement models: Executive Level: Amazon Q demonstrations, QuickSight analytics with generative BI, art-of-the-possible sessions. Technical Level: Lakehouse architecture workshops, platform assessments (Databricks vs Snowflake vs AWS-native), migration planning. Lead comprehensive consultative engagements that begin with generative AI vision (Amazon Q, Bedrock) and translate into concrete data modernization roadmaps. Develop proposals that balance innovative AI capabilities with foundational data platform requirements. Guide customers through parallel journeys: generative AI adoption (POCs to production) and data platform modernization (legacy to Lakehouse). Collaborate with sales teams to position both solution portfolios strategically based on customer maturity and needs. Technical Excellence & Market Awareness Maintain deep expertise across both solution domains: Generative AI **: Amazon Bedrock, Amazon Q, QuickSight Q, SageMaker JumpStart, prompt engineering, RAG architectures, vector databases. Data Platforms **: AWS Glue, SageMaker Unified Studio, Databricks on AWS, Snowflake on AWS, Redshift, EMR, Apache Iceberg, Delta Lake. Position AWS solutions effectively against other cloud platforms' offerings in both generative AI (Azure OpenAI, Vertex AI) and data platforms (Azure Synapse, BigQuery) Guide architectural decisions on build vs. buy for both Al capabilities and data platform components Dual Expertise Required: Deep experience with generative AI technologies: Amazon Bedrock, Amazon Q, LLM architectures, RAG implementations. Proven track record delivering data modernization: Lakehouse architectures, Databricks and/or Snowflake implementations, AWS Glue/EMR deployments A bachelor's degree in computer science, Data Science, Engineering, Mathematics, or a related technical field is required. At the manager’s discretion, additional relevant experience may substitute for the degree requirement. A minimum of 15 years of enterprise solution architecture experience. A minimum of 8 years of public cloud experience. A minimum of 5 years as a senior-level architect or solutions leader with hands-on experience in both AI/ML and data platform modernization. Proven Presales/Sales Engineering experience. Demonstrated success in engaging C-level executives using generative AI demonstrations while delivering complex data platform transformations. Strong understanding across the full spectrum: AI/ML: Generative AI, foundation models, LLMs, traditional ML, prompt engineering, fine-tuning. Data Platforms **: Lakehouse architectures, data mesh, ETL/ELT, streaming, data governance, data quality. Proficiency in Python, SQL, and Spark with hands-on experience in: Generative AI: LangChain, vector databases, embedding models. Data Engineering: PySpark, Apache Iceberg/Delta Lake, orchestration tools. A proven ability to articulate both visionary AI possibilities and practical data platform requirements to diverse audiences. An advanced degree (Master's or PhD) in a relevant field Experience with AWS professional services or AWS partner ecosystem across both Al and data domains Hands-on experience with: Multiple Lakehouse platforms: Databricks, Snowflake, AWS-native (Glue + Athena + Redshift) Multiple Al platforms: AWS Bedrock, Azure OpenAI, Google Vertex Al Industry certifications: AWS: Solutions Architect Professional, Machine Learning Specialty, Data Analytics Specialty Platform specific: Databricks Certified, Snowflake SnowPro Experience with regulated industries requiring governance for both AI and data platforms Track record building practices that deliver both generative AI solutions and data modernization programs Published thought leadership in generative AI applications and/or modern data architectures