bluCognition
Website:
blucognition.com
Job details:
About bluCognition
bluCognition is an AI-powered commercial credit intelligence company serving lenders and financial institutions. Our platform combines commercial credit data contributed by 150+ lenders with AI-driven analytics, banking data and fraud detection to help our clients make faster, better credit decisions. Our product suite includes bluSense, which converts bank statements into standardized, categorized transaction data with real-time financial insights, and FraudLens, which detects document tampering and first-party fraud. Alongside our data products, we run a managed services business supporting underwriting, fraud review, and KYC/KYB and sanctions screening operations for our clients, with teams across North America, Asia and Europe.
About the Role
We are looking for a hands-on Head of Engineering to own the technology behind our data products and to embed AI across everything we build and operate. This is a product engineering and applied-AI leadership role, not a general technology administration role: information security is managed by a dedicated infosec consultant, and cloud infrastructure and DevOps are managed by an infrastructure consulting partner. Your mandate is focused on three things — advancing our bank statement automation engine, using AI to make our data products smarter and more accurate, and driving AI-led process efficiencies across our managed services operations.
You will lead our engineering and ML teams, set the technical roadmap in partnership with the CEO and product leadership, and be accountable for the accuracy, reliability and scalability of the platforms our clients depend on for credit decisions.
Key Responsibilities
- Bank statement automation: Own the engineering roadmap and delivery for bluSense — document ingestion, OCR/extraction, transaction categorization, analytics and scoring — continuously improving straight-through processing rates, accuracy and turnaround time.
- AI for data products: Identify and ship AI/ML capabilities across our data products — including LLM-based document understanding, fraud signal detection in FraudLens, cash-flow analytics, and enrichment of bureau and banking data — that create measurable client value and product differentiation.
- Managed services efficiency: Partner with operations leadership to apply AI and automation to underwriting support, fraud review and KYC/KYB screening workflows, reducing manual effort per transaction and improving quality, consistency and unit economics.
- Team leadership: Build, mentor and retain a high-performing engineering and data science organization across geographies; establish strong engineering practices for code quality, testing, model validation, release management and documentation.
- Platform architecture: Own the architecture of our product platforms and APIs, ensuring they scale with client and data-partner growth (including exchange partnerships such as SBFE) and integrate cleanly with client systems and fintech platforms.
- Consultant collaboration: Work effectively with our infosec consultant and infrastructure partner — ensuring engineering work meets security, compliance and reliability requirements without owning those functions directly.
- Client engagement: Support sales and client teams in solutioning, technical due diligence and integrations for banks, lenders and fintech partners.
- Metrics and governance: Define and report on engineering and model KPIs — extraction accuracy, model performance, uptime, delivery velocity and automation rates — to the leadership team.
What We Are Looking For
- 12+ years in software/data engineering with 5+ years leading engineering or data science teams, ideally in fintech, lending technology, credit risk or financial data products.
- Proven track record shipping ML/AI-powered products to production — document processing, NLP/LLMs, classification or risk scoring — and improving them iteratively against accuracy and cost metrics.
- Strong grounding in data-intensive architectures: data pipelines, APIs, microservices and modern cloud platforms (AWS/Azure/GCP), with the judgment to direct an outsourced infrastructure partner effectively.
- Experience applying automation/AI to operational workflows (e.g., underwriting operations, document review, BPO/managed services) is a strong advantage.
- Familiarity with the US lending ecosystem — bank statement analysis, commercial credit bureaus, fraud and KYC/KYB — is highly desirable.
- Comfortable operating in a bootstrapped, revenue-focused company: pragmatic, hands-on and outcome-driven rather than title- or headcount-driven.
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