Pay Rate Range: $ 53.03 - 56.81/hr.
Job Description:
• 8+ years of experience in software engineering, analytics, business intelligence, or AI application development.
• Hands-on experience with Microsoft Fabric, including OneLake, Lakehouse or Warehouse, Power BI semantic models, and Power BI Embedded.
• Working knowledge of Fabric IQ concepts, including ontologies, business entities, relationships, graph-based context, and agent-ready semantic layers.
• Strong experience developing modern web applications using React 18.x or 19.x, TypeScript, JavaScript, reusable UI components, and REST APIs.
• Experience building AI-led reports and AI-embedded analytics, including natural-language interaction, automated narratives, anomaly or trend explanations, recommendations, and conversational analytics.
• Hands-on experience with Azure OpenAI or comparable large language model services, prompt engineering, retrieval-augmented generation, semantic search, and AI evaluation.
• Strong understanding of report discovery, persona and use-case analysis, KPI definition, data lineage, semantic modeling, and report rationalization.
• Ability to rapidly prototype 2 to 3 high-value reports and convert discovery findings into epics, features, user stories, acceptance criteria, estimates, dependencies, and a prioritized backlog.
• Experience defining reusable architecture patterns, report templates, React components, prompt libraries, semantic models, and governance controls for enterprise-scale adoption.
• Strong SQL skills; proficiency in Python or C# is preferred. Experience integrating structured and unstructured enterprise data is desirable.
• Excellent facilitation, stakeholder management, communication, and storytelling skills for both technical and executive audiences.
Roles & Responsibilities
• Lead discovery workshops with business, product, data, UX, security, and technology stakeholders to understand current reports, decisions supported, user journeys, pain points, KPIs, data sources, and regulatory constraints.
• Assess the existing reporting landscape and classify reports for modernization, consolidation, redesign, reuse, or retirement using agreed business-value, complexity, risk, and usage criteria.
• Select and develop an initial set of 2 to 3 representative AI-powered report prototypes that demonstrate measurable business value and establish reusable implementation patterns.
• Design AI-led reports that proactively surface insights, drivers, trends, exceptions, contextual narratives, and recommended next actions instead of presenting static metrics alone.
• Build AI-embedded report experiences in React and Power BI Embedded, including conversational interfaces, natural-language exploration, guided analysis, explainable insights, and role-aware experiences.
• Develop and align Fabric IQ ontologies, Power BI semantic models, business definitions, relationships, rules, and actions so reports and AI agents use consistent enterprise context.
• Integrate enterprise data from Microsoft Fabric, APIs, lakehouse or warehouse platforms, operational systems, documents, and approved knowledge sources while maintaining security and lineage.
• Validate prototypes with end users through demonstrations and structured feedback; document business outcomes, functional gaps, technical constraints, adoption considerations, and lessons learned.
• Translate discovery and prototype findings into a delivery-ready backlog containing epics, capabilities, features, user stories, acceptance criteria, technical enablers, non-functional requirements, dependencies, risks, and prioritization rationale.
• Define the roadmap and scalable delivery approach for expanding from the initial prototypes to an approximately 3,000-report estate, including waves, report archetypes, reusable accelerators, automation opportunities, quality gates, and governance.
• Establish development standards for React components, embedded analytics, prompts, semantic models, AI evaluation, accessibility, observability, testing, deployment, and responsible AI.
• Collaborate with product owners and delivery teams on planning, estimation, release sequencing, sprint execution, demos, documentation, and knowledge transfer.
• Measure outcomes such as adoption, decision-cycle improvement, report consolidation, insight quality, response accuracy, performance, and reuse of common assets
Key Responsibilities
1) Use-Case Discovery Forward DeploymentPartner with stakeholders (businessproductcustomers) to identify and shape AI opportunities into well-defined use cases with success metrics| constraints| and rollout plans.
Run workshops and technical discovery to assess feasibility| data readiness| integration needs| and operational risks.
Drive rapid prototyping| pilot deployments| and iterative improvements based on real user feedback.
2) Applied ML Engineering (Classic ML Deep Learning)Develop and improve ML solutions (classification| regression| ranking| forecasting| anomaly detection| NLP).
Establish and maintain robust evaluation practices offline metrics| validation strategies| experimentation| and AB testing.
Perform feature engineering| error analysis| model optimization| and performance tuning for production requirements.
3) GenAI LLM Engineering (If Applicable)Build and productionize RAG (Retrieval-Augmented Generation) pipelines| including document ingestion| chunking strategy| embeddings| retrieval tuning| reranking| and response grounding.
Implement guardrails and reliability patterns prompt templates| toolfunction calling| hallucination reduction| citation strategies| and fallback paths.
Develop evaluation harnesses for GenAI quality metrics| regression tests| safety tests| and human-in-the-loop workflows.
4) Productionization (MLOps LLMOps)Package models into scalable services and deploy using DockerKubernetes and CICD.
Implement model lifecycle management model registry| versioning| automated retraining triggers| and governance workflows.
Build monitoring and observability drift detection| latencythroughput monitoring| error tracking| alerting| and rollback mechanisms.
5) Systems Integration Platform CollaborationBuild integration layers (RESTgRPC APIs| event-driven services) to embed AI capabilities into products and enterprise workflows.
Collaborate with data engineers to design reliable pipelines and ensure data quality| lineage| and governance.
Ensure secure and compliant design (PIIPHI handling| RBAC| secrets management| encryption| audit trails).
6) Technical Leadership EnablementProvide technical guidance and mentoring to engineers lead design reviews and establish best practices.
Document solutions with architecture diagrams| runbooks| and operational playbooks.
Create reusable accelerators (templates| libraries| patterns) to scale deployments across teams or customers
Essential Skills:
An experienced AIML Forward Deployed Engineer with 8 years of engineering experience to deliver high-impact AIML (and GenAI| where applicable) solutions end-to-end.
You will blend applied machine learning| software engineering| and stakeholder problem-solving to deploy production-grade systems that are scalable| secure| observable| and aligned to business KPIs.
This role is ideal for engineers who enjoy operating at the intersection of data models systems real users| and who can thrive in ambiguous| fast-moving environments
Skills: Digital : Deep Learning~Digital : DevOps Continuous Integration and Continuous Delivery (CI/CD)~Digital : ReactJS~Digital : Microservices~Digital : Spring Boot~Digital : Azure Machine Learning (ML)~Generative AI~AI Agents
Experience Required: 6-8 years
| Skills: | | Category | Name | Required | Importance | Experience |
|---|
| SkillCategoryTest1_MN | AI Agents | Yes | 1 | >7 years | | | SkillCategoryTest1_MN | Digital : Azure Machine Learning (ML) | Yes | 1 | >7 years | | | SkillCategoryTest1_MN | Digital : Deep Learning | Yes | 1 | >7 years | | | SkillCategoryTest1_MN | Digital : DevOps Continuous Integration and Continuous Delivery (CI/CD) | Yes | 1 | >7 years | | | SkillCategoryTest1_MN | Digital : Microservices | Yes | 1 | >7 years | | | SkillCategoryTest1_MN | Digital : ReactJS | Yes | 1 | >7 years | | | SkillCategoryTest1_MN | Digital : Spring Boot | Yes | 1 | >7 years | | | SkillCategoryTest1_MN | Generative AI | Yes | 1 | >7 years | |
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