AI Platform & Systems Integration Engineer

Open Systems Technologies; New York (NY); Presencial; USD 170.000 - 200.000; Jornada completa

Descripción de la vacante

Open Systems Technologies in New York, NY is seeking an AI Platform & Systems Integration Engineer to own end-to-end data pipelines, transformation, indexing, and interfaces connecting DMS and enterprise repositories to vector databases and LLM backends.

You will lead cross-functional delivery, define architecture standards, mentor engineers, and ensure secure, compliant multi-tenant design while optimizing latency and cost.

Formación

• 5+ years in data engineering or backend integration in production systems.
• Technical leadership with roadmaps, design reviews, and mentoring teams.
• Proficiency with RESTful and event-driven architectures (Kafka, queues).

Responsabilidades

• Own end-to-end design of ingestion, transformation, and indexing pipelines.
• Design and maintain REST and event-driven APIs, tool-calling interfaces, and middleware.
• Engineer access-control propagation for multi-tenant environments.
• Establish CDC and synchronization across downstream indices.
• Collaborate with AI evaluation teams to translate failures into tests and benchmarks.
• Lead cross-functional delivery, code reviews, architecture standards, and mentorship.
• Triage production issues across APIs, messaging, and AI orchestrators.

Conocimientos

Data engineering

Backend integration

Distributed platforms

Technical leadership

RESTful APIs

LLM failure modes

Authorization models

Python

Java

C#

AI developer tools

Herramientas

CI/CD

Docker

Git

Automated testing

Kafka

Descripción del empleo

AI Platform & Systems Integration Engineer

Location: New York, NY

Compensation: $170-200K + bonus

Responsibilities

• Own the end-to-end design of ingestion, transformation, and indexing pipelines connecting document management systems (DMS) and enterprise repositories to vector databases and LLM backends.
• Design and maintain production-grade REST and event-driven APIs, tool-calling interfaces (including Model Context Protocol / MCP), and middleware that connect autonomous agents with core business systems.
• Engineer robust access-control propagation to ensure ethical walls, matter restrictions, and granular user permissions remain uncompromised across indexing pipelines, vector stores, and AI responses.
• Establish real-time change-data-capture (CDC) and synchronization routines that reflect document edits, access revocations, retention policies, and deletions across downstream AI indices instantly.
• Collaborate with AI evaluation teams to translate ingestion failure modes, context drift, and retrieval bottlenecks into automated test suites, benchmarking frameworks, and regression controls.
• Lead cross-functional delivery across internal engineers and external vendor teams. Run code reviews, define architecture standards, mentor junior talent, and articulate trade-offs clearly to legal, security, and executive partners.
• Triage complex production issues spanning API gateways, asynchronous message buses, retrieval pipelines, and AI orchestrators, balancing latency, cost, and reliability.

Qualifications

Required

• 5+ years of hands-on experience in data engineering, backend integration, or distributed platform engineering, with a proven track record of taking complex data infrastructure into production.
• Technical leadership experience, having guided technical roadmaps, run design reviews, and mentored engineering teams.
• Mastery of building and securing RESTful and event-driven architectures (Kafka, message queues, webhooks), API gateways, OAuth/OIDC, schema versioning, idempotency, rate limiting, and observability.
• Strong working knowledge of LLM failure modes, unstructured data extraction, vector databases, hybrid search (keyword + dense embeddings), re-ranking strategies, and RAG architectures.
• Experience implementing complex authorization models, multi-tenant isolation, or information barriers in regulated or high-compliance environments.
• Proficiency in Python, Java, or C#, alongside standard engineering workflows (CI/CD, Docker, Git, automated testing frameworks).
• Hands-on familiarity leveraging modern AI developer tools (coding agents, LLM-assisted workflows) responsibly, with strong instincts for context management, security validation, and code scrutiny.

Preferred

• Background in legal tech or highly regulated professional services (e.g., finance, healthcare, consulting).
• Hands-on familiarity with enterprise DMS platforms (such as iManage, NetDocuments), knowledge graphs, legal ontologies, or entity resolution.
• Experience implementing governed agentic tool interfaces (such as Anthropic’s Model Context Protocol or function-calling tool standards).