Technical Project Manager; Remote SymphonyAI Financial Services

About SymphonyAI Financial Services

SymphonyAI Financial Services is the global leader in AI-powered financial crime prevention. We provide SensaAI and Net Reveal, combining AI and non‑AI software, to help over 300 banks, insurers, and financial firms prevent money laundering, fraud, sanctions breaches, and market abuse. Our systems sit between criminals and the financial system and are closely monitored by top regulators around the world.

Our Professional Services and AI Value Delivery organization brings signed contracts to life as active financial crime defenses. We deploy AI within core compliance and fraud operations at some of the world’s most demanding institutions. We integrate with payments, core banking, KYC, and case management systems, and work closely with Chief Compliance Officers, Heads of Financial Crime, and CIOs until they see measurable improvements in alert quality, investigator productivity, and regulatory outcomes.

As a Technical Project Manager, you will own the end‑to‑end delivery of SensaAI and Net Reveal deployments at major financial institutions. You will lead complex, multi‑stream projects spanning transaction monitoring, sanctions screening, KYC and fraud, and act as the single point of accountability for delivery quality, timelines, regulatory readiness, and customer satisfaction.

This is an individual contributor role and an excellent opportunity for someone who has built and deployed technology – ideally in financial services – before moving into project management. You should be equally comfortable reading a transaction monitoring rules library, challenging a bank’s proposed model architecture, guiding a model risk management team through validation evidence, and presenting steering committee updates to a Chief Compliance Officer.

You will be the person the bank trusts to translate reality into actionable project plans and to provide clear, honest guidance on the program.

If you are passionate about an AI‑first, AI Fast approach – treating AI as the default tool rather than a last resort, and moving at the speed technology enables, even within regulated change‑management environments – this is an excellent opportunity for you.

Lead single‑workstream or small multi‑workstream projects within an existing customer engagement, with senior TPM or engagement manager oversight on cross‑team dependencies, escalations, and decisions that affect the broader program.

Run delivery the AI Fast way – short cycles, working software, instrumented decisions, and decisions made on evidence rather than opinion, even inside long bank change‑management calendars.

Maintain rigorous operating cadence: kickoff, weekly delivery, risk and issue management, change control, financial tracking, lessons learned, and regulator‑grade documentation expected in financial services, working in partnership with the broader delivery team.

Contribute to delivery of assets – playbooks, accelerators, model tuning patterns, MRM evidence templates, and reference deployments – that the rest of the Financial Services AI Value Delivery practice can reuse.

Use AI tools in your own workflow – for planning, status synthesis, risk analysis, alert and scenario review, customer artifacts, and quality checks. We expect AI‑first behavior from the people delivering AI.

Deploy AI financial crime technology successfully, in production.

Typically up to $1 M in budget, operating within a defined scope and existing contract – from kickoff through model tuning, user acceptance, go‑live, and hyper‑care – and ensuring alignment on schedule, budget, quality, and risk. Engage senior TPMs for scope‑change decisions, commercial trade‑offs, and complex risk calls.

Orchestrate cross‑functional teams (solutions architecture, data science, engineering, and customer stakeholders) to ensure the technical solution is coherent, compliant, and delivers against agreed financial crime objectives, challenging assumptions where needed and driving decision‑making.

Drive AI deployment readiness for regulated environments: data…