• Design and develop software applications that incorporate AI and Machine Learning models; • Integrate AI models with existing systems, APIs, data pipelines, and distributed architectures; • Collaborate with data scientists and data engineers in building, training, versioning, and deploying models; • Implement MLOps practices, ensuring automation, monitoring, traceability, and scalability of models in production; • Develop solutions based on LLMs (Large Language Models) such as chatbots, recommendation systems, classification, semantic search, and intelligent automation; • Ensure code quality, security, performance, and software engineering best practices; • Participate in architectural decisions, technical reviews, and definition of standards; • Work on continuous improvement of solutions, evaluating performance metrics of models and software; • Document technical solutions and share knowledge with the team.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Spade is looking for a full stack engineer who thrives at the intersection of data infrastructure and developer experience. You'll join a lean, high-ownership team building real-time transaction enrichment for some of fintech's most innovative companies, and have a meaningful impact on both the product and engineering culture from day one. You will: - Collaborate on our technical vision to build a product customers love - Develop and maintain low-latency, massively-scalable developer facing APIs and a robust data platform supporting our data science team - Continuously improve our engineering best practices by designing and implementing systems that allow us to expand the code base, drive productivity, and scale the team - Debug production issues across all levels of the stack - Bring a strong collaborative approach to deliver value to customers and internal stakeholders at Spade Qualifications - This position is remote friendly (with an option to come to an office if in the NYC area), but only open to applicants based in the United States and Canada. - Must-have: - 5+ years of experience building and scaling backend Python-based systems in fast-paced environments - Proven expertise in at least two of the following areas: infrastructure, data pipelines, and API development - Experience building systems from scratch, making trade-offs, and executing autonomously in early-stage environments - A product mindset and strong problem solving skills, with the ability to navigate ambiguity and focus on delivering customer value - A collaborative mindset, fostering a culture of mentorship, shared success, and continuous improvement - Nice-to-have: - Proficiency in Django and SQL - Familiarity with data science and analytics tools such as Pyspark, Databricks and Delta Lake, and Looker - Experience with cloud infrastructure and tools like AWS CDK - Experience with transaction, merchant, and/or location data - Experience and/or interest in fintech and/or data products Benefits - Competitive compensation and equity package - Full medical, dental, and vision benefits for US-based employees - Life & short-term disability insurance - Unlimited PTO - 401K for retirement planning - Remote-friendly team, with pet-friendly headquarters in NYC - Paid parental leave - Work from home stipend Diversity & Inclusion at Spade Spade is an equal opportunity employer, committed to building a culture that is diverse, equitable, and inclusive. We believe that having people with different backgrounds, experiences, abilities, and perspectives not only helps us build the best products for our customers, but also helps us be the best version of ourselves. Salary Range At Spade, we view total compensation as consisting of salary + equity + benefits. We recruit motivated and high performing talent, and work to compensate people in line with the value they bring to our team. We aim to pay fairly and competitively, and consider a number of factors in developing compensation offers. These factors include years and breadth of experience, interview performance, market dynamics, and internal equity. The anticipated cash salary range for this role for candidates based in the United States is between $180,000 and $220,000 USD (based on level), with an equity grant.

• Ship flagship agentic capabilities: Deliver high-impact agentic workflows end-to-end, from discovery through production roll-out, with clear success metrics and fast iteration loops. • Build and operate production-grade agent systems: Design reliable agentic systems that behave predictably under real-world constraints, including latency, cost, data quality, and failure modes, with strong patterns for state management, idempotency, and safe recovery. • Create shared foundations for agent delivery: Develop the core primitives that enable teams to build agents consistently (runtime patterns, tool interfaces, context management, shared libraries) while avoiding one-off implementations. • Establish a pragmatic Agent Development Life Cycle (ADLC): Implement evaluations, guardrails, tracing, monitoring, and release processes so agents can be measured, debugged, and improved continuously. • Integrate ML and LLM components into production workflows: Work with ML/Data teams to productionise models and LLM capabilities with clear contracts, versioning, observability, and safe degradation patterns. • Maintain clear domain boundaries as adoption scales: Define shared semantics for agent tools and data access, preventing domain drift while enabling teams to move quickly. • Collaborate with Platform on infrastructure and developer tooling: Adopt and extend existing CI/CD, DevEx, and observability systems, contributing back where agentic workloads introduce new requirements.

• Lead and mentor team focusing on work planning, technical approach, design assurance, execution excellence • Lead integrated approaches and solutions across diverse plant disciplines or specialties • Coordinate across matrixed Engineering teams to assess RFIs/RFPs • Support business and product line leaders in ITO commercial risk review process • Collaborate proactively and inclusively for overall team across other engineering disciplines