• Build and evolve shared AI Platform capabilities • Improve LLM Ops and ML Ops capabilities • Design and implement systems for performance and reliability • Identify tooling gaps and standardize best practices • Collaborate closely with cross-functional teams • Evaluate emerging AI tools and models

Why Socure? Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day. We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading. About the Role As a Senior Software Engineer on the Agentic AI Foundations team, you will be one of the founding engineers responsible for designing and building the core platform, systems, and primitives that enable Socure to transition from traditional software workflows to agent-native operations. You will work across applied AI, platform engineering, and system design to create a secure, evaluable, vendor-agnostic agent platform that other teams across Socure can build on. This is a zero-to-one role with outsized impact: you will shape the architecture, reliability, and safety of agentic systems that power critical internal workflows and, over time, customer-facing products. You will be expected to operate with a high degree of ownership and autonomy, partnering closely with leaders in Platform, Product, Data Science, and other engineering teams to deliver high-impact agent-powered workflows and establish best practices for building secure, observable, and scalable agent systems. What You’ll Do: - Design and build a vendor-agnostic agent platform, including orchestration, tool use, memory, and runtime systems that can be reused across multiple workflows and teams. - Develop evaluation and reliability frameworks (metrics, harnesses, pipelines) to measure and improve agent performance, robustness, and safety in production. - Implement safety and governance controls such as guardrails, policy enforcement, and human-in-the-loop review mechanisms to ensure responsible agent behavior and compliance with internal and external standards. - Build systems for data grounding, retrieval, and memory that enable agents to be accurate, context-aware, and aligned with Socure’s domain knowledge and policies. - Prototype, iterate on, and productionize agent behaviors including planning, multi-step execution, and coordination of tools and services, using real internal workflows as proving grounds. - Partner with product and engineering teams across Socure to identify high-impact use cases, jointly design agent-powered workflows, and launch them into production using the platform primitives you build. - Define and document best practices, design patterns, and paved paths for building secure, observable, and scalable agent systems, and mentor other engineers on how to apply them. - Contribute to the team’s strategy and roadmap by informing architecture choices, identifying technical risks, and helping prioritize foundational investments (e.g., tracing, evaluation approaches, dev tooling). - Uphold and model Socure’s leadership expectations for senior engineers: operating with strong product and systems thinking, influencing without authority, collaborating across functions, and raising the technical bar for the team. What You’ll Bring - 5+ years of professional software engineering experience with a strong background in large-scale distributed systems, backend platforms, or infrastructure. - 3+ years of experience designing, building, and operating production-grade systems with clear reliability, performance, and observability requirements. - Hands-on experience with LLMs, Agentic AI systems, or building intelligent applications (e.g., using modern LLM APIs, orchestration frameworks, or ML-powered services in production). - Demonstrated ability to operate in ambiguity and build from first principles in zero-to-one or highly novel problem areas, including making sound trade-offs under uncertainty. - Strong product and systems thinking: you can connect technical decisions to real-world impact, understand user and business needs, and design systems that balance speed, quality, and safety. - Familiarity with AI safety, security, or policy systems such as guardrails, content filtering, access controls, or audit and compliance mechanisms. - Proficiency in at least one modern backend programming language and ecosystem (e.g., Java, Go, Python, or similar) and comfort working with cloud-native infrastructure, APIs, and data services. - Experience collaborating with cross-functional partners (e.g., product, data science, platform, security) to deliver complex technical initiatives. Preferred Qualifications - Background in multi-agent systems, workflow orchestration, or similar distributed coordination frameworks. - Exposure to building or using agent platforms (e.g., orchestration frameworks, tool registries, memory systems) or advanced prompt/LLM routing, caching, or fine-tuning pipelines. - Experience with evaluation frameworks, experimentation platforms, or ML systems (e.g., offline/online evals, A/B testing, or benchmarking agents and models). - Experience specifically with AI safety, security, or policy systems (guardrails, policy engines, content filters, or responsible AI frameworks). - Experience with retrieval systems, knowledge graphs, or data platforms used to ground LLMs and agents in enterprise context. - Experience in a founding, early-stage, or platform-building role where you defined patterns and paved paths for other teams. Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly. Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly. Follow Us! YouTube | LinkedIn | X (Twitter) | Facebook

• Drive greater efficiency and scalability though the design and implementation of workflows that connect GTM signals (intent, engagement, enrichment, lifecycle events) to outcomes and replace manual, inconsistent processes with standardized, measurable operating patterns. Examples include: - Signal-driven lead/account qualification + routing (e.g., intent + ICP fit → owner/sequence/next best action), with clear SLAs and exception handling. - Research + personalization automation that leverages existing systems and pre-built agent concepts (research agents, prospecting agents, sales↔marketing feedback agents, cross-sell agents) to reduce rep/admin time and improve message quality and consistency. - Partner motion automation tied to partner objects, sourcing taxonomy, and reporting readiness (partner data → dashboards → actions) to improve pipeline attribution and partner execution. • Build and maintain reliable integrations, automations, and data sync patterns across the GTM stack, improving data quality, reducing tool sprawl, and ensuring actions are triggered from trusted signals. • Establish an AI/automation operating cadence for GTM: intake → prioritization → build → QA → release → measure → iterate, with clear owners, SLAs, and documentation to reduce ad hoc requests and rework. • Instrumentation and monitoring for automations/agents (throughput, failure modes, fallbacks, and human-in-the-loop paths) so GTM teams can trust and adopt what’s shipped. • Define and track success metrics for each workflow (time saved, speed-to-lead, conversion lift, pipeline impact, data quality), and partner with Analytics to make results visible in Power BI. • Drive adoption through enablement: stakeholder training, playbooks, change management, and feedback loops that turn prototypes into repeatable, scalable workflows used day-to-day.

About the role This is not a role where you will bolt AI onto an existing product as an afterthought. SqlDBM is rebuilding core workflows around AI — from how data architects design schemas to how engineers validate changes to how governance teams maintain compliance across enterprise environments. You will work at the intersection of .NET backend engineering and applied AI — building the systems that make AI a first-class, reliable, enterprise-grade capability inside one of the most technically demanding categories in data infrastructure. This role sits inside our dedicated AI group and reports directly to senior leadership. You will have a short line to product, architecture, and business decisions — no layers, no queue. The team is small by design, moves fast, and has direct visibility into enterprise customer needs. What makes this role different You are not building a chatbot. You are building AI infrastructure for enterprise data teams — systems that understand schema context, generate governed artifacts, and integrate into the workflows of the world's most sophisticated engineering organizations. The problems are hard. The customers are demanding. The work is meaningful. What you'll do You will own and extend the backend systems that power SqlDBM's AI capabilities. Specific areas include: - AI-assisted data modeling workflows — backend services that allow users to describe their data needs in natural language and receive accurate, governed schema output - Context-aware intelligence — systems that use the full richness of a user's data model, naming standards, and governance rules to produce AI output that is specific to their environment, not generic - Automated documentation and metadata generation — AI pipelines that analyze existing schemas and produce accurate, consistent business documentation at scale - Integration with enterprise AI ecosystems — API layers that allow external AI agents and tools to call SqlDBM as a trusted source of schema context - Consumption tracking and orchestration — backend infrastructure that manages AI request routing, model selection, cost optimization, and usage metering - Governance-aware AI workflows — systems that embed approval, validation, and compliance logic into AI-generated outputs before they reach production - MCP server development — building and extending SqlDBM's Model Context Protocol server so that external AI agents and LLM-based tools can use SqlDBM as a trusted, real-time schema authority - Tech Stack includes: C# / .NET AWS PostgreSQL Redis REST APIs LLM Integration MCP Microservices Our backend is built on modern .NET with a microservices architecture deployed on AWS. AI capabilities are built on top of major LLM providers via API, with proprietary prompt engineering, context management, and output validation layers that are core intellectual property of the platform. Qualifications - 5+ years of backend engineering experience with C# and .NET - Proficiency in English - Strong understanding of REST API design and asynchronous service architectures - Experience integrating with external APIs and managing complex data pipelines - Comfort working with LLMs via API — understanding of prompt construction, context management, token economics, and output validation - Experience building systems that handle variable, structured data — schemas, metadata, or similar - Strong engineering fundamentals — testing, code review, system design, observability - Ability to work independently in a remote-first, fast-moving engineering team Strong Plus - Experience with data modeling, database design, or data engineering tooling - Familiarity with enterprise data platforms — Snowflake, Databricks, dbt, or similar - Background building developer tools or platforms used by technical teams - Experience with agentic AI workflows, tool-use patterns, or AI infrastructure - Knowledge of semantic layer concepts, ontologies, or structured metadata systems - Familiarity with Model Context Protocol (MCP) — building or consuming MCP servers in agentic AI architectures What We Offer - Competitive base salary and equity in a profitable, growing company - Fully remote — work from anywhere - Direct impact on product direction — small team, no layers, your work ships to enterprise customers - Work on genuinely hard technical problems at the frontier of AI and enterprise data infrastructure - Collaborative, engineering-driven culture that moves fast and trusts its people Why now SqlDBM is at an inflection point. We have enterprise customers, a profitable business, and a product that is becoming something significantly more powerful. The engineers who join now will shape what that means and build the systems that define the next chapter of data architecture tooling.