Software Product Pre Sales Engineer Senior Manager - FinCrime

Accenture•Chicago, IL

About The Position

The Senior Manager, Pre-Sales supports complex software sales cycles by delivering product demonstrations, responding to technical and functional requirements, and developing reusable pre-sales assets. The role combines product expertise and client-facing skills across direct engagements, partner-supported opportunities, and industry events to help advance pipeline conversion. The position focuses on Accenture’s Fincrime OS. It requires the ability to explain the product’s financial crime capabilities and architecture, tailor demonstrations to client needs, engage technical, risk, and compliance stakeholders, and help shape pilots and proofs of value. The role also contributes to solution sizing, commercial deal models, technical due diligence, and continuous improvement of pre-sales materials and practices. Travel may be required, ranging from 0% to 80% depending on client and business needs.

Requirements

At least 8 years of pre-sales, solution consulting, or technical sales support experience in enterprise software.
A track record of contributing to complex sales opportunities, including RFP responses and solution demonstrations.
At least 8 years of experience with enterprise software delivery models such as SaaS, licensed software, and managed services, as well as integration approaches.
At least 12 months of demonstrated pre-sales experience supporting AI-native software products built on LLMs, agentic workflows, or machine learning platforms, with evidence of demonstrations delivered or RFP contributions.
At least 1 year of hands-on AI coding or development experience.
Bachelor’s degree or equivalent, including a minimum of 12 years of work experience; candidates with an associate degree must have at least 6 years of work experience.
Deep functional knowledge of financial crime processes and Fincrime OS capabilities across KYC/CDD/EDD, AML alert investigation, and sanctions and watchlist hit adjudication.
Ability to run end-to-end technical demonstrations of Fincrime OS and tailor scenarios to clients’ products, channels, and detection estate.
Ability to explain the distinction between Financial Crime agents, rules-based detection, and ML detection, including agent investigation and adjudication, rules-based routing and coverage, and traceability to source data and documents.
Understanding of entity resolution and validation, including matching parties across sources, ownership and control structures, and the effects of data quality on screening and investigation.
Ability to design and present a Fincrime OS deployment architecture involving relevant client systems, external data sources, identity and single sign-on, and downstream systems.
Understanding of file-, API-, and event-based data ingestion, including client-specific adapters that map formats to 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.
Experience engaging technical, risk, and compliance stakeholders, including financial crime technology, enterprise architecture, security, model risk, data, 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.
Ability to demonstrate agent quality through labeled historical cases, recall and precision results against an agreed recall-first quality bar, evidence citations, decision rationale, and governed policy changes.
Familiarity with data requirements for Financial Crime agents, including alert and case payloads, customer and entity data, documents, lists, adverse media, data quality assessment, and data preparation before go-live.
Experience scoping pilots and proofs of value for financial crime operations, including defining scope, agreeing holdout sets and success criteria, running parallel evaluations, and presenting results to relevant stakeholders.
Ability to shape solutions for outcome-based managed services and SaaS-style or AI-as-a-Service deals.
Experience leading technical responses to RFPs, security questionnaires, and due diligence reviews.
Experience presenting technical solutions to mid-level and C-suite stakeholders.
Strong written and verbal communication skills, including the ability to produce high-quality proposal content under time pressure.
Organized and detail-oriented, with the ability to manage multiple concurrent opportunities.
Self-motivated and resilient, with the ability to thrive in a fast-paced environment.

Nice To Haves

Hands-on experience configuring or building AI agents, including Python, prompt and agent configuration, building and running evaluation sets, and interpreting agent traces for clients.
AI certifications, such as Microsoft AI-900/AI-102, Google Cloud Professional ML Engineer, AWS Certified ML Specialty, or DeepLearning.AI specialisations.

Responsibilities

Support the end-to-end pre-sales process for assigned opportunities, including discovery, demonstrations, RFP responses, solution scoping, and proposal preparation.
Deliver tailored product demonstrations to mid-level and senior client stakeholders, translating product features into business value.
Facilitate client workshops to identify requirements, validate product fit, and shape the solution approach.
Prepare 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 into the pre-sales playbook.
Support channel and technology partners with product demonstrations, technical guidance, solution positioning, and partner-led opportunities requiring additional pre-sales support.
Represent the business at industry events and trade shows by demonstrating products, engaging prospects and clients, and documenting leads and follow-up actions.
Develop and maintain reusable pre-sales materials, including demo scripts, solution blueprints, proposal modules, competitive battle cards, and ROI templates.
Stay current on product updates, competitors, and industry trends, and share insights with the broader sales team.
Contribute to onboarding new team members on product and solution knowledge.
Work 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, and support pipeline reporting.
Participate in win/loss reviews and apply findings to improve proposal quality and demonstration effectiveness.
Demonstrate Fincrime OS capabilities across KYC/CDD/EDD, AML alert investigation, sanctions and watchlist adjudication, fraud operations, case management, agent workflows, and SAR and regulatory reporting support.
Design and present Fincrime OS solution architectures, including integrations, data ingestion patterns, cloud-native AI components, identity and single sign-on, and downstream case and reporting systems.
Explain agent quality and behavior, including evaluation results, evidence citations, decision rationale, and policy configuration and governance.
Scope pilots and proofs of value, including process and case populations, holdout sets, quality bars, success criteria, parallel runs, and stakeholder presentations.
Shape solutions for outcome-based managed services, SaaS-style, and AI-as-a-Service deal models, and provide sizing input to the commercial team.
Lead technical responses to RFPs, security questionnaires, and due diligence reviews covering architecture, information security, data residency, resilience, outsourcing, and model governance.
Travel as required by client and business needs, potentially 0–80%.