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
Role Descriptions: Key Responsibilities1) 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
Desirable Skills:
Keyword:
Skills: Digital : Deep LearningDigital : DevOps Continuous Integration and Continuous Delivery (CICD)Digital : ReactJSDigital : MicroservicesDigital : Spring BootDigital : Azure Machine Learning (ML)Generative AIAI Agents
Experience Required: 6-8
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