• Run and optimize our self-hosted inference stack • Run the inference serving layer on our own GPU hardware: choose and tune the serving stack (vLLM, SGLang, TensorRT-LLM) for high throughput and low latency. • Optimize aggressively: tensor parallelism, quantization (FP8, AWQ, GPTQ), KV-cache and prefix caching, continuous batching, speculative decoding, concurrency tuning. • Serve multiple models and features off shared hardware: multi-LoRA, routing, and request scheduling that balances internal workloads against latency-sensitive product traffic. • Keep our AI fast, efficient, and observable • Make our AI workloads efficient: improve latency, throughput, and GPU utilization so we get the most out of what we run. • Build the visibility: instrument performance and usage across our AI surfaces so there's clear data on how everything is running. • Surface the technical tradeoffs (performance, latency, efficiency) so the people making the calls have what they need to make them. • Build AI features and proactive agents • Ship the in-app agent layer that helps families coordinate: proactive nudges, smart suggestions, agents that summarize, draft, schedule, and act for busy parents. • Build the substrate underneath: tools, memory, orchestration, guardrails, and evaluation harnesses, integrated cleanly with production APIs alongside our architecture team. • Work in nimble pairs with feature owners, standing up whatever's needed to test an idea, including a vibe-coded UI when that's the fastest path to a real customer. Ship rough, learn fast, harden what works.

• Run and optimize our self-hosted inference stack • Run the inference serving layer on our own GPU hardware: choose and tune the serving stack (vLLM, SGLang, TensorRT-LLM) for high throughput and low latency. • Optimize aggressively: tensor parallelism, quantization (FP8, AWQ, GPTQ), KV-cache and prefix caching, continuous batching, speculative decoding, concurrency tuning. • Serve multiple models and features off shared hardware: multi-LoRA, routing, and request scheduling that balances internal workloads against latency-sensitive product traffic. • Make our AI workloads efficient: improve latency, throughput, and GPU utilization so we get the most out of what we run. • Build the visibility: instrument performance and usage across our AI surfaces so there's clear data on how everything is running. • Surface the technical tradeoffs (performance, latency, efficiency) so the people making the calls have what they need to make them. • Ship the in-app agent layer that helps families coordinate: proactive nudges, smart suggestions, agents that summarize, draft, schedule, and act for busy parents. • Build the substrate underneath: tools, memory, orchestration, guardrails, and evaluation harnesses, integrated cleanly with production APIs alongside our architecture team. • Work in nimble pairs with feature owners, standing up whatever's needed to test an idea, including a vibe-coded UI when that's the fastest path to a real customer. Ship rough, learn fast, harden what works.

Role Description This role is responsible for designing, developing, implementing, troubleshooting, and optimizing scalable, high-performance software and product applications. Leveraging industry best practices, the Sr. Software & Product Engineer delivers robust, customer-focused solutions that accelerate product innovation. - Assesses and defines software and product requirements, establishing the specifications and standards that guide scalable, high-quality development. - Executes coding, debugging, testing, and troubleshooting across the full development lifecycle, incorporating AI-assisted and agentic development approaches to maintain quality and delivery efficiency. - Develops and advances software and product capabilities that integrate with design systems, infrastructure, databases, and cloud-based platforms, all with the goal of maximizing operational efficiency. - Evaluates application requirements and architects database solutions that ensure scalability, performance, and data integrity. - Serves as a subject matter expert in AI-assisted development practices; acts as a resource for the engineering team on the effective and disciplined application of AI coding tools, informs development standards around AI use, and reinforces quality expectations through code reviews. Qualifications - Bachelor's degree (or international equivalent) or equivalent experience, required. - 5+ years of related experience, required. - 2+ years of Agentic Engineering required. - Experience with Claude Code, Curser or comparable LLM. - 5+ Python required. - Experience with Git/GitHub, JIRA, Confluence, CircleCI, required. - Experience developing in Agile, SCRUM, or similar iterative methodologies, required. - Experience in fast-growing companies or entrepreneurial environments, required. - 9+ years of related experience, preferred. - Demonstrated knowledge of component-based frontend architecture and modern frontend development principles, enabling scalable, modular front-end development. - Skilled in frontend build tools and development pipeline practices. - Advanced knowledge of software testing methodologies enabling robust, reliable test coverage. - Expert-level knowledge of AI-assisted development tools and agentic coding workflows, with the ability to apply engineering judgment to evaluate, refine, and integrate AI-generated code into production software delivery. - Technical proficiency to translate detailed business requirements into actionable technical specifications and determine the most effective implementation approach using a wide range of tools and technologies. - Industry knowledge of current software engineering practices and emerging development methodologies. - Ability to serve as a technical resource for the engineering team on AI-assisted development practices, guide peer adoption of effective AI tool use, and reinforce quality standards for AI-generated code. - Ability to influence and contribute to architectural design. - Ability to travel less than 5% of the time. - Must be 18 years of age or older. - Must successfully complete pre-employment screening process, as required. - Must successfully complete any required training or orientation courses, as needed. Benefits - Work from anywhere – Thryv is a Remote First company! - Competitive medical, dental, and vision plans, plus a wellness program with added incentives. - 401(k) savings plan with company match and employee stock purchase plan. - Continuing education benefits with tuition assistance programs. - One week of paid time off at the end of the year, in addition to our standard paid time off policy.

• Lead the technical architecture of Data, Analytics and AI solutions for our clients, covering the full lifecycle from design to deployment: • Design end-to-end data architectures: data lakes, lakehouses, warehouses, and streaming pipelines. • Define standards for data modeling, storage, ingestion, and transformation across client engagements. • Architect MLOps and AI deployment infrastructure (model registries, CI/CD for ML, monitoring). • Lead technical decisions on cloud platforms (Azure, AWS, GCP) and open-source tooling. • Define best practices and reusable frameworks for data engineers, analysts, and data scientists. • Act as a technical mentor and reviewer for cross-functional project teams. • Bridge the gap between data analysts, data engineers, and AI/ML engineers on complex projects. • Contribute to internal knowledge base, toolkits, and delivery accelerators. • Lead architecture workshops and discovery sessions with client stakeholders. • Translate business requirements into scalable, robust technical blueprints. • Present architecture decisions to both technical teams and executive audiences. • Support pre-sales and proposal efforts with technical scoping and solution design. • Provide internal training and knowledge-sharing sessions with the team. • Support the Head of Practice on business development and internal capability initiatives.