Aubrant Digital

Full-stack Engineer

• Own the technical success of an engagement, ensuring architectural coherence and reuse across services, data, and AI components
• Drive technical decisions and document them as ADRs: database engine selection, service vs library boundaries, and integration patterns
• Lead architecture review boards and resolve cross-team design dependencies
• Set engineering standards: trunk-based development, IaC (Terraform/Bicep), CI gates, SLSA L2 provenance, OpenAPI 3.x and AsyncAPI 2.x contracts, and idempotent integration patterns
• Lead code and design reviews; enforce quality gates and SOC 2 control mappings (logical access, monitoring, change management, audit trail)
• Plan and orchestrate delivery cadence, including sequencing of build and release windows for engagements with production blackout constraints
• Partner with client product owners to translate domain knowledge into technical designs
• Mentor engineers on Azure architecture, event sourcing, durable orchestration, and HITL design patterns
• Run agile delivery in 2-week sprints with monthly stakeholder demos, hypercare planning, and post-go-live support

Technical Lead

Frontend Engineer

• Design and build complex, responsive, and accessible user interfaces in Vue 3 using TypeScript, modern JavaScript (ES2020+), HTML5, CSS3, and Sass.
• Architect component state, composition, and reusability patterns; write highly optimized, modular components that scale across applications.
• Profile and resolve front-end performance bottlenecks across rendering, bundle size, and runtime behavior.
• Translate Figma design system outputs into production code, leveraging auto-layout, variables, components, and Dev Mode handoff to maintain fidelity from design to implementation.
• Build and maintain reusable UI component libraries and design tokens that codify the design system across products and teams.
• Lead implementation of WCAG 2.1 AA compliant interfaces using semantic HTML, ARIA, keyboard navigation, and screen reader testing.
• Conduct accessibility audits, define remediation plans, and coach the team on accessibility best practices.
• Champion code quality through code review, automated testing, linting, and front-end engineering standards.

Senior Front-End Engineer

DevOps Engineer

• Design and maintain SQL Server 2019+ schemas, indexes, and query patterns that meet performance, scalability, and concurrency requirements.
• Analyze execution plans, identify bottlenecks, and tune queries, indexes, and statistics; advise on RCSI and isolation level choices.
• Design and operate Change Data Capture (CDC) pipelines and other change-tracking patterns to support downstream consumers.
• Implement and review data access using linq2db, LINQ-to-SQL (DBML), Entity Framework, and ADO.NET; choose the right tool for each scenario.
• Translate business and application requirements into efficient SQL and C# data access code that is performant, testable, and maintainable.
• Partner with application engineers to align ORM usage with database performance characteristics and avoid common anti-patterns.
• Design and operate data solutions on Azure, including Azure SQL, storage, networking, and security configurations.
• Contribute to CI/CD for database changes through migration scripts, schema versioning, and automated deployments.
• Mentor engineers on database design, query optimization, and data access patterns; set standards for the team.

Senior DevOps Engineer

Data Engineer

Senior Data Engineer

Machine Learning Engineer

• Build reusable AI skills consumed across engagements: Document Intelligence, document summarization, data normalization, anomaly detection, matching engines, and compliance test runners
• Design and train custom Document Intelligence neural models for client-specific document types
• Implement RAG over enterprise document libraries using Azure AI Search with hybrid vector + keyword retrieval and semantic ranking
• Build LLM reasoning chains using Azure OpenAI (GPT-4o for complex reasoning, GPT-4o-mini for high-volume classification) with prompt versioning and guardrails
• Design agent orchestration in Azure AI Foundry for multi-step workflows: extract, search, reason, and generate output with tool-use grounding
• Build evaluation harnesses, accuracy thresholds, and drift detection; tie outputs to confidence-gated HITL review tiers
• Implement audit trail patterns for AI-assisted workloads: prompt/response logging, evidence chains, and SOC 2 aligned event sourcing
• Mentor engineers on prompt engineering, RAG design, agentic patterns, and evaluation; contribute to Aubrant's AI engineering standards

Senior AI/ML Engineer

SDET

• Design and build full test harnesses for .NET solutions, including end-to-end, integration, and API tests using Playwright and complementary frameworks
• Maintain and extend existing test suites, refactoring for stability, reusability, and clear failure diagnostics
• Partner with developers to make applications testable by design, including hooks, identifiers, and contract patterns
• Integrate test execution into Azure DevOps pipelines with appropriate parallelization, reporting, and quality gates
• Run and troubleshoot test workloads against Azure-hosted environments, including handling environment-specific configuration
• Contribute to release confidence by tying test outcomes to deployment decisions and feedback loops
• Pilot and advance agentic automation patterns, AI-assisted test generation, and self-healing test approaches that improve coverage and reduce maintenance
• Contribute to QA standards, tooling, and patterns shared across Aubrant engagements
• Participate in test planning, defect triage, and quality reviews with engineering and product teams

QA Automation Lead

• Own end-to-end delivery of the data transformation
• Translate architecture into an executable plan
• Drive sprint planning and milestone tracking
• Act as point of contact for customer leadership
• Lead and contribute to data modeling across enterprise domains
• Build production-grade ETL/ELT pipelines on Azure Databricks
• Partner with cloud and DevOps team on data needs
• Define the QA approach for data quality

Architect – Data Engineering

Artificial Intelligence

• 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.