Software Product Pre Sales Engineer Senior Manager - FinCrime

• Support the end-to-end pre-sales process for assigned opportunities: discovery, demos, RFP responses, solution scoping and proposal preparation.

• Deliver tailored product demonstrations to mid-level and senior client stakeholders, translating features into business value.

• Facilitate client workshops to identify requirements, validate fit and shape the solution approach.

• Prepare written responses to RFPs, RFIs and security questionnaires in coordination with product, legal and delivery teams.

• Support proof-of-concept engagements; document outcomes and feed learnings back into the pre-sales playbook.

• Support proof-of-concept engagements; document outcomes and feed learnings back into the pre-sales playbook.

• Support channel and technology partners in pre-sales engagements: provide product demonstrations and technical guidance, build their confidence in positioning the solution, and assist on partner-led opportunities when additional pre-sales depth is required.

• Support the business at industry events and trade shows: deliver product demonstrations for event audiences, engage prospects and clients on-site with relevant use cases, and document leads and follow-up actions promptly after each event.

• Support the business at industry events and trade shows: deliver product demonstrations for event audiences, engage prospects and clients on-site with relevant use cases, and document leads and follow-up actions promptly after each event.

• Develop and maintain reusable pre-sales materials: demo scripts, solution blueprints, proposal modules, competitive battle cards and ROI templates.

• Stay current on product updates, competitive landscape and industry trends; share insights with the broader sales team.

• Contribute to the onboarding of new team members on product and solution knowledge.

• Contribute to the onboarding of new team members on product and solution knowledge.

• Work closely with the sales lead and AD Pre-Sales to align on opportunity strategy and solution approach.

• Maintain accurate records of pre-sales activities and solution configurations; support pipeline reporting as required.

• Participate in win/loss reviews and apply learnings to improve proposal quality and demo effectiveness.

• Deep functional knowledge of Fincrime OS across KYC/CDD/EDD, AML alert investigation, and sanctions and watchlist hit adjudication. This includes the analyst workbench, case management, deterministic rules and workflow orchestration, Financial Crime agents, and SAR and regulatory reporting support.
• Working knowledge of fraud operations, including fraud alert investigation, case handling and how fraud and AML processes connect.
• Ability to run end-to-end technical demonstrations of Fincrime OS across a representative scenario. The flow runs from alert or case intake through agent investigation and evidence gathering, analyst review and decision, and escalation and SAR support, to management reporting. Each demo is tailored to the client's products, channels and existing detection estate.
• Ability to explain clearly how Financial Crime agents differ from rules-based and ML detection. Agents investigate and adjudicate inbound alerts rather than originate them. Deterministic rules and workflow govern routing and coverage, and every agent finding traces back to source data and documents.
• Understanding of entity resolution and entity validation in financial crime. This covers matching customers, counterparties and related parties across sources, establishing ownership and control structures, and how entity data quality affects screening and investigation outcomes.
• Ability to design and present a solution architecture for a Fincrime OS deployment. This includes integration with existing transaction monitoring and screening engines, onboarding and core banking systems, document management, and external data sources such as sanctions lists, corporate registries and adverse media. It also covers identity and single sign-on, and downstream case and reporting systems.
• Understanding of file, API and event-based data ingestion patterns, including a client-specific adapter that maps each client's formats into a stable canonical data model.
• Familiarity with cloud-native AI architectures on a major cloud provider, including serverless and containerized compute, managed LLM inference, event streaming, environment isolation, observability and data residency considerations.
• Experience engaging technical, risk and compliance stakeholders. These include the Head of Financial Crime Technology, Enterprise Architect, CISO and security teams, Model Risk, Chief Data Officer, Head of Compliance Operations and third-party risk teams.
• Familiarity with model risk management for AI agents, including:
• SR 11-7 and PRA SS1/23 alignment
• agent versioning and validation
• independent challenge and ongoing monitoring
• model cards and per-decision explainability for regulators and auditors

• Ability to demonstrate agent quality and behavior. This means running agents against labeled historical cases and presenting recall and precision results against an agreed recall-first quality bar. It also means showing evidence citations and decision rationale, and explaining how policy changes are configured, reviewed, approved and versioned.
• Familiarity with the data requirements for Financial Crime agents, including:
• alert and case payloads from detection engines
• customer and entity data
• document types, lists and adverse media sources
• data quality assessment and the data preparation typically needed before go-live

• Experience scoping pilots and proofs of value for financial crime operations, including:
• defining in-scope processes and case populations
• agreeing the holdout set, quality bar and success criteria
• running a parallel run against current analyst decisions over a meaningful period
• presenting results to operations, compliance and model risk stakeholders

• Ability to shape the solution for each deal model:
• outcome-based managed services, including the operating model with analysts in the loop, SLAs and capacity
• SaaS-style and AI-as-a-Service deals, including platform scope and client responsibilities

• Experience leading technical responses to RFPs, security questionnaires and due diligence reviews. These cover architecture, information security, data residency, resilience, outsourcing and model governance.

• Minimum 8+ years of pre-sales, solution consulting or technical sales support experience in enterprise software including a track record of contributing to complex sales opportunities, including RFP responses and solution demonstrations.
• Minimum 8+ years of enterprise software delivery models (SaaS, license, managed services) and integration approaches.
• Minimum 12 months of demonstrated experience supporting pre-sales for AI-native software products — solutions built on LLMs, agentic workflows or machine learning platforms — with evidence of demos delivered or RFP contributions.
• Minimum 1 year experience with hands-on AI coding/development experience
• Bachelor’s degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)

• Hands-on experience configuring or building AI agents. This includes Python, prompt and agent configuration, building and running evaluation sets, and interpreting agent traces to explain behavior to clients.

• Experience presenting technical solutions to mid-level and C-suite stakeholders.

• Strong written and verbal communication skills; able to produce high-quality proposal content under time pressure.

• Organized and detail-oriented; comfortable managing multiple concurrent opportunities.
• Self-motivated and resilient; able to thrive in a fast-paced environment
• AI certifications (e.g. Microsoft AI-900/AI-102, Google Cloud Professional ML Engineer, AWS Certified ML Specialty, DeepLearning.AI specialisations) are valued.