SPD Technology

• Work with GenAI & LLMs: Design, develop, and fine-tune generative AI models using transformer architectures. Implement advanced techniques to create practical solutions for real-world challenges. • Use Modern Toolkits: Leverage frameworks like LangChain, LangGraph, and LangSmith to build automated pipelines and intelligent agents. Integrate these tools with vector databases for efficient similarity search and real-time analysis. • Extract Data Insights: Process and analyze large volumes of unstructured data using embeddings and statistical modeling to identify meaningful insights. • Build Scalable Systems: Collaborate with cross-functional teams to create production-grade ML systems that handle real-time, high-volume data processing. • Mentor & Lead: Act as a mentor for junior team members and stay updated on trends in Agentic AI, transformer models, and generative systems. • Collaborate & Communicate: Work with Product and Engineering teams to improve data management and support innovation. Present complex analyses clearly and effectively.

• Lead the technical discovery process and design robust, scalable, cost-effective solutions • Own estimation end-to-end • Act as the bridge between Sales and Delivery • Prepare technical sections of proposals • Produce an architecture vision, roadmap, MVP definition and high-level delivery plan • Own and evolve the end-to-end architecture of solutions • Drive system evolution toward well-defined target and transition (interim) states • Participate in starting new projects from scratch • Customize and guide the harness and agent setup to fit a team’s SDLC • Drive system innovation by leveraging AI as a core enabler

Role Description This is an internal architecture role at the intersection of pre-sales, delivery and AI enablement. Around 60% of our incoming pre-sales requests now involve AI/ML (OCR, Computer Vision, fraud detection, RAG and agentic solutions), so we need an architect who can shape, challenge, estimate and sell these solutions — not a narrow ML specialist. You will work across multiple engagements and domains rather than a single product, and collaborate closely with our in-house ML/MLOps team rather than owning deep ML modelling yourself. The role has three focuses: - Pre-Sales & Estimation — shaping and defending solutions and estimates for new engagements. - Solution Architecture & SDLC ownership — owning end-to-end architecture and the overall target-architecture vision across engagements. - AI Enablement — helping teams adopt AI-assisted development: selecting the right tools/harness per project, onboarding, upskilling, and monitoring adoption. Qualifications - 7+ years of hands-on software development experience with strong core engineering principles, including 3+ years designing complex software architectures for multi-stack environments. - Broad, hands-on engineering experience across the full SDLC: distributed systems, cloud-native architectures, scalable APIs, data-driven solutions and enterprise integrations. - Practical experience designing data solutions across relational databases, distributed caching, replication and enterprise search engines. - Experience architecting on at least one major cloud platform using containerization and orchestration, plus a working map of managed cloud services and the habit of continuously learning new platforms, frameworks and libraries — commercial and open source. - Practical, hands-on understanding of AI/GenAI and agentic development — agents, LLM behaviour, context management, AI coding harnesses and their trade-offs. This is central to the role. - Ability to work effectively alongside ML engineers on AI/ML solutions (OCR/Computer Vision, fraud detection, RAG, agentic) — enough to scope, challenge and estimate, without being a deep ML modelling specialist. - A disciplined approach to architectural decision-making: systematically evaluating trade-offs, risks and technology pros/cons to deliver resilient, cost-effective solutions. - Strong estimation skills and the ability to defend estimates to both Sales and Delivery. - Proven ability to manage stakeholder expectations and build cross-organizational alignment, adapting from deep technical dives with engineers to non-technical dialogue with senior stakeholders. - Excellent communication and client-facing skills; people-oriented and comfortable guiding, onboarding and upskilling engineers — enablement is a core part of the job. - Strong analytical and problem-solving skills, with the ability to define technical solutions under tight deadlines and vague requirements. - Prior experience in pre-sales, technical discovery or solution consulting. - Experience coaching engineering teams and growing their ability to make high-quality autonomous software-design decisions. Requirements - Lead the technical discovery process and design robust, scalable, cost-effective solutions for proposals, RFI/RFP responses and new client engagements. - Own estimation end-to-end: turn Sales’ discoveries into engineering-grounded estimates; challenge and correct estimates that are not grounded in real delivery effort. - Act as the bridge between Sales and Delivery — ensure estimates reflect engineers’ input and that the delivery team understands and can execute what was sold. - Prepare technical sections of proposals: solution descriptions, architecture diagrams, assumptions, risks and delivery approach. - Be the primary technical point of contact for prospective clients, clearly articulating the solution, technology stack and implementation strategy to technical and non-technical stakeholders. - Produce an architecture vision, roadmap, MVP definition and high-level delivery plan during discovery/inception. - Where AI/ML is involved, scope and estimate it correctly, working with our in-house ML engineers for deep modelling input. - Own and evolve the end-to-end architecture of solutions (backend services, data storage, integrations, cloud infrastructure), explicitly addressing the "-ilities": scalability, availability, recoverability, maintainability, extensibility, portability, usability and security. - Hold and promote the target architecture vision across the portfolio; run architecture reviews and design workshops with delivery teams. - Drive system evolution toward well-defined target and transition (interim) states, using system diagrams that give engineering teams a clear, actionable execution path. - Propose pragmatic, balanced technical decisions in areas such as build vs. buy, now vs. later and refactor vs. rebuild, and document the trade-offs behind them. - Define and maintain architecture artefacts: high-level and system diagrams, data flows, non-functional requirements, technical guidelines and ADRs — and author AI-ready solution specifications as part of the design lifecycle. - Provide hands-on guidance: design sessions, review of critical technical decisions and PRs, spikes and PoCs when needed. - Ensure the solution meets scalability, security, availability and cost-efficiency expectations, and simplify otherwise complex problems into pragmatic designs. - Participate in starting new projects from scratch: scope, architecture approach, MVP slice and key technical decisions. - Understand AI-assisted development deeply: how agents and agentic workflows work, how LLMs behave, context management, and the trade-offs/gaps of AI coding tools and harnesses (e.g., BMAD, Spec Kit) — including limitations such as incomplete TDD, combined dev+test single-agent roles, and context handling. - Recommend which AI dev tools/harness fit which class of project, phase and SDLC — and where they do not; identify gaps and judge whether they are critical for a given project. - Customize and guide the harness and agent setup to fit a team’s SDLC, rather than blindly adopting a ready-made flow. - Support AI enablement across projects: initial guidance, onboarding of teams (incl. SPD Labs), upskilling engineers and AI champions, and monitoring adoption while answering questions along the way. - Drive system innovation by leveraging AI as a core enabler — prototyping high-impact capabilities to prove technical feasibility and steer future-ready architectural directions. - Provide early, defensible ballpark estimates using AI tooling to help qualify opportunities before deeper discovery. Benefits - Join the team of experts who create custom, cutting-edge tech solutions for world-renowned businesses, fueling client growth. - Enjoy the freedom of fully remote work with a flexible working schedule. - Empower yourself with a stable workload and a stable income, supported by provided laptops and licensed software. - Benefit from performance and merit reviews, elevate your skills with personal development plans, and individual learnings through the corporate library, public speaking support, and more. - Work with a team of one mind who cares about what they do and how they do. - Collaborate with top-notch experts who are always ready to help and support you through any challenges. - Join company-wide tech and cultural events, and contribute to meaningful CSR initiatives that resonate with your values. - Feel supported by your HR, and take advantage of our referral bonus program.

• Serve as a force multiplier for development teams by creating golden paths that remove roadblocks and improve ideation and innovation. • Collaborate with other engineers, product managers, and internal stakeholders in an Agile environment. • Provide mentorship, technical guidance, and perform code reviews for team members. • Design and deliver on projects end-to-end with little to no guidance. • Provide support to teams building and deploying AI applications by addressing common pain points in the MLDLC. • Learn constantly and be passionate about discovering new tools, technologies, libraries, and frameworks (commercial and open source), that can be leveraged to improve PitchBook’s AI capabilities. • Support the vision and values of the company through role modeling and encouraging desired behaviors. • Participate in various cross-functional company initiatives and projects as requested. • Contribute to strategic planning in a way that ensures the team is building exceptional products that bring real business value. • Evaluate frameworks, vendors, and tools that can be used to optimize processes and costs with minimal guidance.

• Manage and grow multiple engineering teams and their leaders within a business unit or product area. • Cultivate technical excellence and a culture of continuous learning across teams. • Participate in and supervise discussions on key technical decisions, delivery expectations, and people allocation. • Represent the development direction at the strategic (VP) level, including plans, results, metrics, and capabilities. • Drive AI tool adoption to improve team efficiency and delivery speed. • Encourage learning through AI-assisted development and automation. • Coach teams on best practices for software delivery, estimation, stakeholder communication, and continuous improvement. • Ensure clear goals, priorities, and resource allocation while maintaining technical excellence and innovation. • Foster a culture of trust, transparency, collaboration, and constructive feedback. • Maintain high morale and motivation, especially during challenging circumstances. • Balance business, team, and individual needs through effective communication and decision-making. • Recognize team achievements and help teams learn from challenges or failures. • Manage staffing, including hiring new team members aligned with company values and culture. • Monitor and assess team performance, conduct performance reviews, and ensure fair compensation processes. • Support the adoption of DevOps practices and modern engineering methodologies.

• Serve as a force multiplier for development teams by creating golden paths that remove roadblocks and improve ideation and innovation • Collaborate with other engineers, product managers, and internal stakeholders in an Agile environment • Design and deliver tasks end-to-end with little guidance from Senior team members • Provide support to teams building and deploying AI applications by addressing common pain points in the MLDLC • Learn constantly and be passionate about discovering new tools, technologies, libraries, and frameworks (commercial and open source), that can be leveraged to improve PitchBook’s AI capabilities • Support the vision and values of the company through role modeling and encouraging desired behaviors. • Participate in various cross-functional company initiatives and projects as requested. • Evaluate frameworks, vendors, and tools that can be used to optimize processes and costs with minimal guidance.

• We are looking to expand our footprint across key European markets. • Responsible for bringing new revenue to the company by working with multiple sales channels. • Selling high-demand service lines: AI-enabled and Agentic software development, Fintech engineering services, and AI product development. • Initiation and development of long-term, trust-based relationships with clients. • Analyze target markets, identify high-potential segments, and develop targets. • Use various channels for pipeline generation: direct outreach, ecosystem partners, events, and referrals. • Partner with Presales and Delivery teams on discovery, problem framing, and solution shaping.

• Building an AI-powered legal content platform that helps law firms research, draft, and submit Chambers USA rankings. • Capture matter intelligence from court records, documents, and lawyer interviews and turns it into structured knowledge. • Design and build endpoints across the full workflow: matter creation, document ingestion, knowledge extraction, draft generation, review chains, and export. • Build workflows, handle retries and failure states, and keep async jobs from blocking the editorial flow. • Write migrations, optimize queries, and keep the model clean as the product evolves. • Build the layer that orchestrates AI agents, passes context correctly, logs AI activity, and enforces pre-flight review gates. • Manage job queuing and notifications with Celery or equivalent, ensuring traceable records from source to export.

• Design, develop, and maintain features for a production B2B SaaS platform. • Build solutions across both backend (Python/Django) and frontend (Vue.js). • Translate product requirements and Jira tickets into clean, scalable, and maintainable code. • Collaborate closely with engineers, product managers, and designers within a Kanban workflow. • Deliver new functionality, resolve defects, and continuously improve the platform. • Take ownership of features from implementation through production release. • Participate in technical discussions, backlog refinement, stand-ups, and delivery planning. • Contribute to code quality, maintainability, and engineering best practices. • Leverage AI-assisted development tools where appropriate to improve productivity and delivery speed.

• Partner with Business Leadership: Act as a strategic advisor to Directors, Senior Managers, and Business Leaders, aligning people strategy with business objectives, influencing organizational decisions, and supporting leaders in solving complex business and people challenges. • Partner with Clients: Develop trusted relationships with client stakeholders, proactively understand their business needs, advise on people-related topics, support workforce planning and organizational changes, and ensure a consistently high-quality people experience for both consultants and clients. • Drive Organizational Effectiveness: Lead organizational design initiatives, support business transformation, optimize team structures, and identify opportunities to improve organizational effectiveness, collaboration, and business performance. • Lead Workforce Planning & Talent Strategy: Partner with business leaders to develop workforce plans, succession strategies, internal mobility initiatives, and talent development approaches that support long-term business growth. • Lead Organizational Change: Drive complex organizational change initiatives by developing change strategies, communication plans, stakeholder engagement approaches, and adoption activities while minimizing organizational disruption. • Leverage People Analytics: Analyze workforce data, engagement results, turnover trends, and business metrics to identify risks, provide actionable insights, and support data-driven business decisions. • Govern Performance & Compensation Processes: Ensure effective execution of performance reviews, promotions, compensation planning, and talent calibration processes while maintaining consistency, fairness, business alignment, and compliance with company policies and local labor legislation. • Drive Employee Experience: Develop and execute employee engagement initiatives, analyze organizational health, identify improvement opportunities, and partner with leaders to build action plans that strengthen employee satisfaction, retention, and overall experience. • Coach & Develop Leaders: Coach managers and senior leaders on leadership effectiveness, performance management, employee development, difficult conversations, conflict resolution, and organizational leadership practices. • Manage Employee Relations & Organizational Risks: Lead complex employee relations cases, workplace investigations, conflict resolution, organizational changes, and sensitive people matters while ensuring compliance, fairness, and risk mitigation. • Manage a People Business Partners Team: Lead the People Business Partners team by setting priorities, providing coaching and performance feedback, supporting professional development, and fostering a collaborative, high-performing environment. • Collaborate Across Functions: Partner closely with Talent Acquisition, Learning & Development, Compensation & Benefits, Finance, Legal, Operations, Security, and other People functions to deliver integrated people solutions and continuously improve employee experience. • Drive Continuous Improvement: Identify opportunities to simplify, automate, and improve People processes by leveraging technology, AI tools, and best HR practices while contributing to the continuous evolution of the People function.