2 days ago
Base Salary
$225k - $280k/yr
Responsibilities
- Design, implement, test, and operate backend services for agent orchestration, task execution, state management, model integrations, and investigation workflows.
- Build durable synchronous and asynchronous workflows with queues, retries, resumability, cancellation, idempotency, scheduling, and failure recovery.
- Develop agent frameworks, developer harnesses, tool-calling patterns, structured outputs, reusable skills, human review, and policy-controlled execution.
- Build knowledge-ingestion, enrichment, grounding, retrieval, context-selection, memory, and provenance capabilities across structured and unstructured data.
- Develop knowledge-graph, graph-traversal, semantic-search, lexical-search, hybrid-retrieval, reranking, entity-aware, and temporal-retrieval workflows.
- Integrate agents with APIs, identity and risk data sources, case-management and watchlist systems, model endpoints, webhooks, and event streams.
- Implement 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 for investigation-related capabilities.
- Build evaluation and regression tests for retrieval quality, grounding, citation correctness, model behavior, tool selection, workflow completion, and investigation outcomes.
- Instrument services with logs, metrics, traces, latency measurements, cost attribution, retrieval diagnostics, model-version tracking, and operational dashboards.
- Participate in on-call and incident response, improving monitoring, service-level objectives, capacity planning, and recovery procedures.
- Write technical designs, document decisions, review code and designs, and collaborate with product, engineering, operations, security, privacy, and data-science partners.
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 Python, Java, Go, TypeScript, or a comparable backend or data-intensive programming 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 building backend services, agent components, developer tooling, data pipelines, or reusable platform capabilities.
- Experience grounding AI or agent workflows with domain knowledge, entity context, policies, case history, live data, or model outputs.
- 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 concepts including supervised learning, classification, ranking, embeddings, feature engineering, model evaluation, data quality, and model drift.
- Experience supporting machine-learning 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 relational 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.
- Preferred experience includes AI or ML systems for fraud, identity, financial crime, compliance, trust and safety, investigations, or regulated domains.
- Preferred experience includes agent frameworks, model gateways, evaluation and tracing platforms, GraphRAG, enterprise search, MCP-style connectors, workflow engines, durable execution, or distributed task processing.
- Preferred technologies and experience include AWS Bedrock, SageMaker, EKS, vLLM, GPU-backed inference, Neo4j, Neptune, OpenSearch, Elasticsearch, model monitoring, data drift detection, model governance, and responsible AI practices.
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.