About Aubrant 

Aubrant Digital is a leader in multi-shore custom application development. We are passionate about solving our clients’ business problems through consultative teamwork, innovative software, and proven processes. We’ve served more than 50 clients and delivered hundreds of high quality, custom enterprise applications.  Our clients value us as integral team members who get the job done on time and on spec, and we are proud of our high client retention rate and under 2% staff turnover. With offices in New Jersey, Boston, Costa Rica, and Eastern Europe, we execute the full software lifecycle, from architecture and design through development, QA and application maintenance & support.  Our company culture emphasizes client service, trust-based relationships, and innovation. 

Job Description: 

As the Data and AI Studio Leader, you will serve as the most senior data and AI engineer at Aubrant Digital, setting the technical vision, engineering standards, and innovation agenda for the entire Data and AI Engineering Studio.  This include building our capabilities in Aubrant’s Workbench product.  You will be deeply hands-on with customer projects 50% of your time, delivering production-grade data platforms, analytics architectures, and AI/ML solutions. You will also play a critical role in pre-sales, partnering with Advisory and Business Development to shape proposals, lead technical discovery sessions, and demonstrate Aubrant's data and AI capabilities to prospective clients. This role demands someone who builds, not someone who only advises, and who can shift seamlessly between architecting an Azure Synapse lakehouse, training a team on LangChain agent patterns, and presenting a data strategy to a CTO. 

What qualifications make you an Aubrant Data and AI Studio Leader? 

1. A hands-on technical leader who stays close to the code, data, and models; you lead by doing, not by delegating exclusively. 

1. A compelling communicator who can articulate complex data and AI architectures to executive audiences during pre-sales engagements, building confidence and winning new business. 

1. A relentless innovator who continuously evaluates emerging frameworks, model architectures, and cloud services to keep Aubrant and its clients at the leading edge. 

1. A builder of engineering culture who mentors Studio Principals and Members, establishes best practices, and raises the technical bar across every engagement. 

1. Comfortable operating in ambiguity, whether scoping a greenfield data platform for a new client or navigating competing priorities across multiple active engagements. 

1. A customer-obsessed partner who builds deep trust with clients, understands their business context, and translates that understanding into data and AI solutions that deliver measurable outcomes. 

Responsibilities: 

Studio Leadership and Strategy: 

• 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. 

Hands-On Technical Customer Delivery: 

• Architect, build, and deploy production-grade data platforms, analytics pipelines, machine learning models, and AI-powered solutions for enterprise clients, primarily on Azure (with AWS and multi-cloud as needed). 

• 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. 

Pre-Sales and Business Development: 

• 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. 

Innovation and Workbench Contribution: 

• 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 (Azure AI Foundry, Amazon Bedrock, open-source LLMs), 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. 

Qualifications: 

• 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.