AI Engineer

• Define and own end-to-end enterprise architecture for a Microsoft-based customer engagement and CCaaS platform
• Establish target-state architecture across sales, service, channels, integrations, data, and security
• Develop architecture principles, standards, patterns, and governance guardrails
• Architect solutions across Dynamics 365 Sales, Customer Service, and Omnichannel
• Design end-to-end customer and seller journeys including Lead-to-opportunity, Opportunity-to-close, Account management, Case management and resolution
• Define architecture for: Sales automation, Case management and service workflows, Routing, queues, workstreams, and skills-based distribution
• Architect solutions leveraging Power Platform, Microsoft Teams and Microsoft 365
• Define patterns for integration with third-party and ISV platforms
• Define AI governance including prompt design, security, and responsible AI controls
• Organize and oversee architecture authority across programs and workstreams.

Dynamics 365 Enterprise AI Architect

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains
• Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems
• Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics
• Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes
• Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact
• Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics
• Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes
• Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes
• Drive adoption and usage of shared data assets across business units
• Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization)
• Ensure solutions are aligned to real-world workflows and deliver tangible outcomes
• Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships
• Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases
• Ensure alignment of definitions across domains to reduce fragmentation and duplication
• Support both structured and unstructured data integration, enabling downstream AI and analytics applications
• Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships
• Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions
• Own the end-to-end product lifecycle from discovery and definition to delivery and iteration
• Define product requirements, success metrics, and release plans
• Continuously refine the semantic data product based on user feedback and evolving business needs
• Define critical metrics to measure adoption, usability, and impact of the semantic data product
• Supervise usage and continuously improve accessibility and value delivery
• Evangelize the value of connected semantic data across the organization
• Partner with data governance teams to establish standards, definitions, and data quality expectations
• Promote consistency, reusability, and scalability of data assets across the enterprise

Senior Manager, AI Lead – Semantic Layer

• Design and implement scalable APIs for agent orchestration and learner interaction
• Build data pipelines that feed agent evaluation and continuous improvement cycles
• Create event streaming infrastructure for real-time learner interaction analysis
• Ensure data quality and accessibility for training and evaluating learning agents
• Improve data infrastructure for cost and performance in close coordination with Data and ML teams
• Design and implement rigorous evaluation frameworks to measure agent performance and improve cycles
• Develop LLM evaluation processes and perform error analysis to identify systemic improvements for agentic learning systems
• Support instrumentation that makes evaluation relevant (metrics, traces/logs, and analysis workflows)
• Participate in on-call rotation and incident response for learning platform reliability
• Contribute to runbooks, postmortems, and reliability improvements as we evolve our operating model

Senior Software AI Engineer

• Design, develop, and maintain both front-end interfaces and backend services with AI capabilities
• Create and manage RESTful APIs and integrate AI services such as LLMs, embeddings, and document parsing tools
• Extract, transform, and analyze large datasets to enhance product features and user experience
• Implement highly optimized machine learning pipelines with quantized models for legacy hardware
• Ensure high code quality through rigorous testing, code reviews, and debugging
• Proactively solve technical issues and contribute to all phases of the development lifecycle
• Stay updated with emerging technologies and incorporate them into our operations where beneficial

Senior Agentic AI Engineer

• design and build AI‑powered solutions using Microsoft’s AI ecosystem, primarily Azure, GitHub, Copilot Studio, and Azure AI Foundry
• develop and integrate conversational copilots, retrieval‑augmented generation (RAG) experiences, and automation workflows into enterprise applications
• collaborate closely with product, engineering, security, and business stakeholders to deliver scalable, compliant, and measurable AI capabilities that support utility and energy customers
• Build and maintain AI solutions leveraging Azure AI Foundry, including model selection, prompt orchestration, evaluation, and deployment patterns, with a focus on reliable and customer‑centric outcomes
• develop and configure copilots using Copilot Studio, including topics, actions, plugins/connectors, and inclusive conversation design
• Implement AI integration patterns such as retrieval‑augmented generation (RAG), tool/function calling, and agent‑based workflows into applications and internal tooling
• create and maintain API services (for example, Azure Functions, App Service, and containerized services) to expose AI capabilities to enterprise business systems
• design and implement secure and accountable data access patterns for enterprise knowledge sources, including SharePoint, Azure SQL, and Blob Storage
• Use Azure DevOps and GitHub for source control, pull requests, code reviews, branching strategies, and release management in a collaborative and agile environment
• Build CI/CD pipelines that support automated testing, security scanning, and deployments to Azure
• Apply engineering best practices, including unit and integration testing, telemetry, error handling, performance tuning, and clear documentation
• Follow secure‑by‑design practices, including identity and access management (Entra ID), secrets management (Key Vault), and least‑privilege access
• Support responsible AI and governance requirements, including logging, auditing, evaluation, content safety, and prompt and security guardrails
• partner with security and compliance teams to ensure AI solutions align with enterprise policies and regulatory requirements common in the utilities and energy sector
• translate business needs into AI solution designs, user stories, and technical delivery plans that are practical and measurable
• work cross‑functionally with product, engineering, and business partners to define success metrics, validate outcomes, and iterate based on feedback and analytics
• provide guidance and enablement to teams adopting Copilot Studio and Azure AI Foundry patterns, fostering shared ownership and continuous learning

AI Developer

• Develop comprehensive systems and frameworks for AI applications and products.
• Construct prototypes and minimum viable products to validate AI/ML solutions before committing substantial resources.
• Develop, refine, and productionize AI systems using cloud resources such as Azure OpenAI, Azure ML, and Cognitive Services.
• Contribute to the creation of robust data pipelines for model evolution and production systems.
• Develop specifications for low latency APIs and services necessary to deploy AI models and incorporate them into applications.
• Supervise and sustain AI systems in production to guarantee consistent model accuracy and reliability.
• Actively participate in a team that exercises principled, agile-like development practices.
• Create and maintain thorough documentation that is consistent with team procedures, corporate policies, and expectations.
• Ensure peer review on all assigned work, as well as conduct peer reviews over the work of others as requested.
• Guide junior AI engineers on AI/ML and industry best practices and methodologies.
• Keep abreast of new tools and concepts through reading documentation or literature and actively practicing skills development.
• Support and participate in meetings with external stakeholders.

AI Engineer – III

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

• Lead End‑to‑End Solution Delivery: Own delivery end‑to‑end: requirements → architecture → design → build → test → deployment
• Serve as the technical lead: set engineering standards, unblock delivery, and ensure quality across testing and release
• Design and implement AI solutions using Azure AI services (e.g., Azure OpenAI, Cognitive Services, Azure Machine Learning) and, where appropriate, Copilot / Copilot Studio
• Emphasize security, governance, and real‑world usability over experimentation for its own sake
• Build data platforms using Azure data services (ADF, Databricks, Synapse, ADLS) and/or Microsoft Fabric
• Deliver production‑grade pipelines (ETL/ELT), storage and compute patterns, and data models supporting BI and AI workloads
• Use Power Platform (Power Apps, Power Automate, Power BI) when it’s the right tool for rapid business value
• Partner with engineers and business stakeholders to translate requirements into buildable designs
• Coordinate with security and governance teams to ensure solutions meet best practices and client constraints
• Communicate architecture decisions — including tradeoffs — in plain language
• Act as a trusted advisor: align scope to outcomes, manage expectations, and keep clients informed when things change
• Create reusable internal assets (reference architectures, accelerators, playbooks) that improve delivery quality
• Stay current on Azure AI and data platform changes and apply learning pragmatically to client solutions
• Support early discovery, solution envisioning, and contribution to technical proposals, estimates, and SOWs as needed.

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