2 days ago
Base Salary
$230k - $270k/yr
Responsibilities
- Design, implement, test, and operate backend services for agent orchestration, task execution, state management, model integration, and investigation workflows.
- Build reliable synchronous and asynchronous workflows with queues, retries, resumability, cancellation, idempotency, scheduling, and failure recovery.
- Develop agent frameworks, developer harnesses, tool-calling patterns, structured outputs, human review, and policy-controlled execution.
- Build knowledge-ingestion, enrichment, retrieval, knowledge-graph, context-selection, provenance, and domain-grounding capabilities.
- Integrate agents with APIs, identity and risk data, case-management and watchlist systems, model endpoints, webhooks, and event streams.
- Develop secure connectors and tool execution with authentication, authorization, credential management, tenant isolation, audit logging, and sandboxing.
- Integrate, train, fine-tune, evaluate, version, deploy, route, and monitor machine-learning models.
- Improve performance through caching, batching, parallel execution, retrieval optimization, context reduction, model selection, and inference configuration.
- Create evaluation and regression tests for retrieval, grounding, citations, model behavior, tool selection, workflows, and investigation outcomes.
- Instrument production systems with logs, metrics, traces, latency and cost measurements, model tracking, and operational dashboards.
- Participate in on-call and incident response, and improve monitoring, service-level objectives, capacity planning, and recovery procedures.
- Write technical designs, review code and designs, document decisions, and collaborate with product, risk, investigations, platform, SRE, security, privacy, and data-science teams.
Requirements
- Bachelor’s or master’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
- Professional experience building software or backend services through implementation, testing, and production operation.
- Experience with backend development, API design, service-oriented architectures, asynchronous programming, testing, debugging, and performance optimization.
- Understanding of distributed systems, including queues, retries, idempotency, timeouts, concurrency, partial failures, and service reliability.
- Experience with a backend or data-intensive programming language such as Python, Java, Go, TypeScript, or a comparable language.
- Experience building applications using large language models, AI agents, tool calling, retrieval-augmented generation, structured generation, or related generative-AI technologies.
- Understanding of agent orchestration, state transitions, task execution, tool use, context management, memory, human escalation, and observability.
- Experience with backend services, agent components, developer tooling, data pipelines, or reusable platform capabilities.
- Practical understanding of embeddings, vector search, lexical search, metadata filtering, hybrid retrieval, reranking, query expansion, or multi-stage retrieval.
- Experience with knowledge graphs, graph databases, entity graphs, graph traversal, graph-enhanced retrieval, entity resolution, ontology or schema design, provenance, or temporal data modeling.
- Experience combining structured data, unstructured documents, search systems, vector retrieval, and live API-based data sources.
- Working knowledge of machine learning, including supervised learning, classification, ranking, embeddings, feature engineering, model evaluation, data quality, and model drift.
- Experience supporting ML workflows such as dataset preparation, labeling, training, fine-tuning, experiment tracking, evaluation, versioning, deployment, or monitoring.
- Experience evaluating and optimizing AI or ML systems for latency, throughput, cost, and quality.
- Experience with cloud infrastructure and containerized environments, preferably AWS and Kubernetes.
- Experience with databases, queues or event streams, object storage, caching systems, search platforms, vector databases, graph databases, or observability platforms.
- Understanding of authentication, authorization, secrets management, tenant isolation, data protection, and privacy-sensitive system design.
- Ability to write maintainable, well-tested code and troubleshoot with logs, metrics, traces, debuggers, and other engineering tools.
- Strong communication, collaboration, ownership, and problem-solving skills in a fast-moving, high-ambiguity environment.
- Preferred experience includes fraud, identity, financial crime, compliance, trust and safety, investigations, regulated domains, agent frameworks, model gateways, GraphRAG, enterprise search, MCP-style connectors, AWS Bedrock, SageMaker, EKS, vLLM, GPU-backed inference, workflow engines, Neo4j, Neptune, OpenSearch, or Elasticsearch.
Tech Stack
Categories
About Socure
Socure builds an AI-driven digital identity verification and fraud prevention platform used by banks, fintechs, e-commerce, telecom, healthcare, and government agencies to automate KYC/AML, document verification, and risk decisioning. It sells enterprise APIs and workflows for onboarding, authentication, and payments fraud mitigation. Founded in 2012 and headquartered in Incline Village, Nevada, Socure is privately held and operates across regulated and high-volume digital markets.