As a Forward Deployed Engineer, you will help move AI-assisted workflows from promising ideas to trusted production systems. You will work directly with large strategic customers and partners, own the path from discovery and technical scoping to system design, build, deployment, evaluation, and adoption, and solve problems where speed, trust, explainability, and control all matter.
This is not a traditional implementation, advisory, or professional services role. You will be an embedded builder: close enough to customer teams to understand how work actually happens, technical enough to build into real environments, and product-minded enough to turn what works into reusable RiskOS capabilities.
What you will do- Build with strategic customers and partners. Work side by side with large enterprises, public sector organizations, marketplaces, gaming platforms, fintechs, and other high-scale digital businesses to understand their workflows, data environments, technical constraints, business goals, and operational pain points.
- Own deployments from prototype to production. Lead discovery, technical scoping, solution design, build, integration, testing, rollout, and adoption for AI-assisted RiskOS workflows in real customer environments.
- Turn complex operations into working systems. Translate customer policies, SOPs, data sources, and operational processes into RiskOS workflows, integrations, data mappings, tool connections, case flows, and agent behaviors that can operate reliably in production.
- Build the connective tissue. Write code, configure systems, debug issues, and integrate with customer infrastructure, APIs, data sources, security boundaries, analyst tools, and operational systems when progress depends on it.
- Evaluate and improve agentic workflows. Help define how agents interact with tools, data, evidence, decisions, analyst queues, and business logic, then tune those workflows through testing, evals, observability, guardrails, feedback, and production learnings.
- Prove measurable customer value. Measure whether deployed workflows improve speed, accuracy, consistency, analyst productivity, review burden, evidence quality, operational control, or other customer-defined outcomes.
- Turn field learning into platform leverage. Surface product gaps, implementation friction, and repeatable patterns, then partner with product, engineering, data science, security, and GTM teams to convert them into reusable templates, playbooks, primitives, and product capabilities.
What you bring- Strong hands-on technical ability. You have experience building practical solutions across APIs, integrations, workflow logic, data mappings, internal tools, scripts, production systems, cloud services, or similar technical surfaces.
- End-to-end ownership. You can take an ambiguous customer problem from discovery to production, making good tradeoffs across scope, speed, quality, risk, and long-term platform leverage.
- Customer-facing problem solving. You have worked directly with enterprise customers, strategic partners, or internal business stakeholders to understand messy problems, define workable solutions, and ship usable outcomes.
- Product-minded engineering judgment. You can move fluidly between architecture, implementation, debugging, user needs, product tradeoffs, operational risk, and stakeholder communication.
- Systems thinking and calm execution. You can break complex workflows into modular, testable, improvable components, spot risks early, and keep teams moving with clarity and follow-through.
- High agency and comfort with ambiguity. You thrive in fast-moving environments where requirements are incomplete, priorities evolve, and the category is still being defined.
- Relevant technical background. You likely have 4+ years of experience in software engineering, forward deployed engineering, solutions engineering, implementation engineering, applied AI engineering, technical consulting, or a similarly hands-on technical role. Experience with Python, SQL, APIs, cloud services, workflow tools, LLM workflows, retrieval systems, tool calling, copilots, evaluation frameworks, observability, or agentic systems is especially helpful.
Preferred qualifications- Experience deploying AI, ML, LLM, or decision-support systems in production environments.
- Experience in fraud, risk, identity verification, KYC, KYB, AML, sanctions, adverse media, transaction monitoring, disputes, trust and safety, or compliance operations.
- Experience working with large financial institutions, government agencies, marketplaces, gaming companies, fintechs, or other complex enterprise customers.
- Experience with workflow orchestration, case management, rules engines, decisioning platforms, operational automation, evaluation pipelines, or customer-specific implementation tooling.
What success looks like in year one- Large strategic customers and partners are live or in advanced rollout with AI-assisted RiskOS workflows that solve meaningful operational problems.
- Deployed workflows show measurable customer value, such as faster investigations, lower manual review burden, better evidence quality, stronger consistency, improved operational control, or higher analyst productivity.
- Customer-specific lessons have been converted into reusable templates, playbooks, evaluation patterns, workflow primitives, implementation accelerators, or product requirements.
- Product and engineering teams have a sharper roadmap based on real deployment learnings, not abstract requirements.
- Socure has a clearer repeatable motion for deploying agentic workflows across fraud, risk, identity, and compliance operations.
