THE OPPORTUNITY AKUVO is seeking a senior, hands-on engineer to lead the technical execution of applied-AI capabilities across AKUVO IQ and the Data & Analytics organization. Working closely with the SVP of Data & Analytics, you will help shape the technical approach and own the architecture, development, integration, evaluation, deployment, and ongoing improvement of AI product functionality and capabilities. You will build AI experiences grounded in governed data, predictive intelligence, customer configuration, and real financial-institution workflows, and support the internal AI agents used across the model- and software-development lifecycles. As the technical owner of the Data team’s applied-AI layer, you will operate with limited technical supervision — setting architecture and standards rather than working to someone else’s design. The role spans applied-AI engineering, production software development, evaluation, observability, and usage analytics, working across product, data, ML, engineering, domain, and compliance teams. LOCATION Local in Malvern/Philadelphia first, widening to surrounding areas such as New Jersey, New York, Delaware, while continuing to expand geographically in a hybrid/remote capacity based on location. KEY RESPONSIBILITIES - Lead the technical execution of AKUVO’s applied-AI strategy, shaping the technical approach for AI capabilities across AKUVO IQ, Data & Analytics products, and related internal workflows. - Own the architecture, development, integration, deployment, and ongoing improvement of AI product functionality — including agent orchestration, tools, retrieval and grounding, structured outputs, and multi-step workflows — integrating with AKUVO IQ, predictive scores, portfolio data, and customer-specific configurations through governed context. - Build configurable AI capabilities and the workflows that turn customer policies, procedures, and requirements into structured, reviewable, versioned configurations — with testing, approval, and rollback. - Develop automated evaluation and guardrails — covering response quality, groundedness, task completion, tool use, policy adherence, and safety, and protecting against prompt injection, data leakage, and unreliable tool execution — partnering with domain specialists to turn real financial-institution scenarios into test datasets. - Build production observability for AI — quality, traces, errors, latency, token consumption, tool activity, usage, and operating cost. - Evaluate models, frameworks, and technical approaches across quality, reliability, security, performance, maintainability, and cost, avoiding unnecessary lock-in to any single provider. - Instrument and analyze how customers use AKUVO’s AI products, and review internal AI-usage analytics across AKUVO, to drive refinement, automation, and prioritized enhancements. - Support the internal agents used by the Data & Analytics team across the model- and software-development lifecycles — requirements, development, testing, regression, documentation, deployment, monitoring, and triage. - Partner across Data Engineering, ML, Product, Platform Engineering, Security, Domain, and Compliance to ground AI in governed data and meet AKUVO’s product, data-protection, and governance requirements. - Produce technical documentation and operational guidance, promote responsible AI-assisted engineering, and keep the architecture reusable and flexible enough to support new capabilities, products, and customer use cases. SKILLS AND EXPERIENCE - 6+ years of professional software engineering, building and operating production applications or services, with strong Python and experience building APIs, backend services, and integrations. - Able to own AKUVO’s applied-AI layer end-to-end with limited technical supervision — setting architecture, evaluation standards, and engineering patterns that others build against, and holding production accountability when AI behaves unexpectedly. - 2+ years of demonstrated, hands-on experience shipping LLM, generative-AI, or agent-based capabilities to production, including agent orchestration, tool or function calling, structured outputs, retrieval and grounding, and multi-step workflows. - Experience building evaluation, testing, monitoring, and observability for production AI, and instrumenting functionality with analytics to guide improvements. - Understanding of AI application risks — unsupported output, prompt injection, data leakage, inappropriate behavior, and unreliable tool execution. - Experience with cloud-based AI platforms and modern software-development practices (source control, CI/CD, environment management, release controls, production support). - Ability to translate product requirements and real-world workflows into scalable designs, and to evaluate fast-changing models and tools without over-depending on one provider. - Strong communication across technical, product, domain, and executive stakeholders; comfort working independently while collaborating across teams; and active, sophisticated use of AI in your own workflow. PREFERRED QUALIFICATIONS - Microsoft Foundry, Azure OpenAI, or a comparable enterprise AI platform, and AI-agent frameworks (e.g., Microsoft Agent Framework, LangGraph, Semantic Kernel). - RAG, vector search, document and policy ingestion, and structured configuration generation; designing configurable or multi-tenant AI products whose behavior varies by customer. - Adversarial testing, tracing, or continuous evaluation, and human-in-the-loop review, approval, and escalation workflows. - Product analytics, telemetry, experimentation, or AI-adoption measurement; integrating AI into an established B2B SaaS product. - Financial-services, lending, servicing, or collections background (2+ years); sensitive, PII, or regulated data; and familiarity with responsible-AI, model-risk, or AI-governance expectations.