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Principal Data – AI Architect
Location
United States
Posted
72 days ago
Salary
0
Seniority
Lead
Job Description
Principal Data – AI Architect
BrightSide
• Own the data and AI architecture across the company, spanning: AI-powered systems and decisioning workflows, Core application platforms, Analytics and reporting layers • Define and evolve canonical data models across clients, employers, partners, financial products, and outcomes • Ensure consistency across transactional systems, analytical platforms, and AI feature layers • Act as the company’s data strategy expert, with deep understanding of: Employer integrations (eligibility, payroll, SSO, identity), Partner integrations and product data, External and enrichment data sources (e.g., credit, public datasets) • Identify which data sources meaningfully compound business and product value, and which do not • Guide integration and platform investments based on data leverage and long-term value, not just feature demand • Design and govern production-grade AI systems, including: LLM-based applications (RAG, prompt orchestration, embeddings, vector stores), Decisioning and automation workflows • Evaluate and govern AI models and platforms (e.g., OpenAI, Anthropic, open-source), balancing: Accuracy and reliability, Cost and latency, Security, privacy, and explainability • Define standards for AI lifecycle management (build, deploy, monitor, iterate, retire) • Act as the decision-maker for data and AI architecture decisions across engineering teams • Partner with the Enterprise Architecture Board to review proposals, surface risks, and ensure coherence • Establish clear architecture standards and review processes that enable teams to move fast without creating long-term risk or technical debt • Lead rapid prototyping and technical discovery to: Test architectural assumptions, Evaluate new AI approaches, Inform investment and roadmap decisions • Personally build or lead proofs-of-concept where needed to drive alignment and reduce uncertainty • Partner closely with the CTO, Product leaders, Analytics, and Engineering to: Translate business strategy into technical direction, Align data, AI, and platform investments, Serve as a trusted technical advisor to executive leadership on data, AI, and platform tradeoffs
Job Requirements
- 10+ years of experience in software engineering, data architecture, or systems architecture, with significant hands-on experience
- Deep expertise in data architecture and data modeling, including relational databases, event-driven systems, and analytical data platforms
- Strong experience designing and operating data platforms at scale, including data lakes/warehouses and real-time pipelines
- Strong experience designing and operating AI/ML systems in production, including LLM-based architectures (RAG, embeddings, vector databases, prompt orchestration)
- Proven experience in regulated environments (fintech, financial services, healthcare, etc.), with an understanding of data privacy, security, and compliance requirements
- Strong in modern cloud architectures (e.g., AWS) and modern data stacks (e.g., Databricks)
- Ability to connect technical decisions to business outcomes, including cost efficiency, scalability, risk mitigation, and customer experience
- Strong communication skills and comfort influencing across engineering, product, and executive leadership
- Experience serving as a Lead Architect in a high-growth or transformation-stage company
- Background in financial systems, payments, lending, or financial data platforms
- Experience with AI governance, model risk management, or explainability frameworks
- Track record of improving engineer productivity through platform and architecture design.
Benefits
- Health insurance
- 401(k) matching
- Flexible working hours
- Paid time off
- Professional development opportunities
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Role Description Lead AI adoption across Transcenda and our clients by designing and implementing AI-native software engineering practices that materially improve delivery speed, quality, and cost efficiency. You will define how modern engineering teams use AI in production and turn that capability into repeatable client offerings. This is a Staff/Principal individual contributor role with significant influence across internal teams, executive stakeholders, and client organizations. - Define and implement AI-assisted engineering practices across Transcenda teams - Establish standards for AI code generation, PRs review, testing, and documentation - Partner with delivery leadership to embed AI tools into the client teams - Advise CTOs and VP of Engineering on AI adoption strategy, tooling and engineering process - Mentor senior engineers on AI-assisted development process - Define the frameworks for measuring the developer productivity gains Qualifications - 10+ years of hands-on software engineering experience leading complex production initiatives - Experience driving engineering productivity or platform improvements across teams or organizations - Hands-on production experience with LLMs, AI coding tools, or agent-based systems, including trade-offs in quality, cost, latency, reliability, and model behavior - Experience working in enterprise or regulated domains where security, compliance, and risk management shape engineering decisions - Strong architectural skills across CI/CD, code review systems, testing strategies, and cloud-native environments (AWS/GCP) - Experience influencing engineering direction without direct authority across senior engineers, tech leads, and engineering leadership - Ability to translate technical change into measurable business outcomes and communicate clearly with both engineers and executives
Energy Systems Engineer - Freelance AI Trainer
MindriftApply → Pass qualification(s) → Join a project → Complete tasks → Get paid. Project time expectations: Tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements; This is an estimate, not a guaranteed workload, and applies only while the project is active. Note: Rates vary based on expertise, skills assessment, location, project needs, and other factors. Higher rates may be offered to highly specialized experts. Lower rates may apply during onboarding or non-core project phases. Payment details are shared per project.
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