Data and AI Studio Leader
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Job Description
Data and AI Studio Leader managing technical vision and projects at Aubrant Digital. Leading hands-on implementation and mentoring within the Data and AI Engineering Studio.
Responsibilities:
- Define and drive the technical vision, engineering standards, and capability roadmap for the Data and AI Engineering Studio, ensuring alignment with Aubrant's Advisory, Studios, and Workbench ecosystem.
- Recruit, mentor, and develop Studio Principals and Members through structured assessments, code reviews, knowledge sharing, and hands-on coaching on real engagements.
- Drive innovation by evaluating and adopting emerging data and AI technologies, frameworks, and patterns; contribute reusable accelerators and reference architectures to Aubrant Workbench.
- Architect, build, and deploy production-grade data platforms, analytics pipelines, machine learning models, and AI-powered solutions for enterprise clients, primarily on Azure.
- Lead complex data and AI engagements end-to-end: discovery, architecture design, hands-on implementation, testing, and production cutover, ensuring solutions meet performance, governance, and security requirements.
- Serve as the technical authority on client engagements, making real-time architectural decisions, resolving escalations, and ensuring delivery quality at the highest billing tier.
- Partner with Advisory and Business Development to lead technical discovery sessions, shape proposals, define solution architectures, and develop effort estimates for data and AI opportunities.
- Present Aubrant's data and AI capabilities to prospective clients, demonstrating technical depth and business acumen in executive-level conversations that build confidence and close deals.
- Develop and maintain reusable pre-sales assets including reference architectures, demo environments, and proof-of-concept frameworks that accelerate the sales cycle.
- Contribute data and AI accelerators to Aubrant Workbench, including reusable data pipeline templates, ML model serving patterns, AI agent frameworks, and governance modules.
- Stay current with the rapidly evolving data and AI landscape: evaluate new services, assess their enterprise readiness, and integrate viable capabilities into Aubrant's delivery model.
- Collaborate with the Woxsen University AI Research Partnership on applied research initiatives, translating academic breakthroughs into practical client solutions.
Requirements:
- Bachelor's Degree in Computer Science, Data Science, Statistics, Mathematics, or a related discipline, or equivalent experience.
- Master's degree or PhD in a quantitative field is a plus.
- MUST be proficient in written and spoken English (85%).
- 12+ years of professional experience in data engineering, analytics engineering, machine learning, or AI solution development, with progressive leadership responsibilities.
- 5+ years of experience leading data and/or AI engineering teams, including mentoring, setting technical standards, and driving capability development.
- Deep, hands-on expertise with the Azure data and AI ecosystem: Azure Synapse Analytics, Azure Data Factory, Azure Databricks, Azure Machine Learning, Azure AI Foundry, Azure Cognitive Services, Azure OpenAI Service, Microsoft Fabric, and related services.
- Significant production experience with AWS data and AI services (Redshift, Glue, SageMaker, Bedrock) and the ability to design cloud-agnostic data architectures.
- Expert-level proficiency in Python and SQL for data engineering, analytics, and ML workloads.
- Experience with Scala or Spark-native languages is a plus.
- Strong expertise in modern data platform architecture: lakehouse patterns, medallion architectures, data mesh, event-driven data pipelines, and real-time streaming (Kafka, Event Hubs, Kinesis).
- Demonstrated experience building and deploying production ML/AI systems, including model training, evaluation, serving, monitoring, and governance.
- Hands-on experience with GenAI and LLM integration patterns: RAG architectures, prompt engineering, LangChain/LangGraph, agent frameworks (including MCP), fine-tuning, and LLM evaluation/governance.
- Strong knowledge of data governance, data quality frameworks, metadata management, and regulatory compliance (HIPAA, SOC 2, GDPR) as applied to data platforms.
- Experience with infrastructure as code (Terraform, Bicep, CloudFormation) and modern DevOps/MLOps practices for data and AI workloads.
- Proven track record of engaging in pre-sales activities: leading technical discovery, authoring proposals, presenting to C-level audiences, and contributing to revenue generation.
- Experience with open-source and cloud-agnostic data tools (Apache Spark, Delta Lake, dbt, Airflow, MLflow, Kubeflow) is highly valued.
- Familiarity with Temporal.io for workflow orchestration is a plus.
- Excellent problem-solving skills, with the ability to analyze complex requirements and propose innovative, practical solutions.
- Strong communication and collaboration skills, with the ability to work effectively across engineering, advisory, and business development teams.
- Dynamic and collaborative mindset with a focus on continuous innovation and growth.
- Ability to anticipate and adopt innovations in data, AI, and cloud technologies.
- Able to build strong customer relationships and deliver customer-centered solutions.
- Operates effectively, even when things are not certain or the way forward is not clear.
Benefits:
- Not specified
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