AI Platform Engineer - Shared AI Capabilities

Landing Point; Village of Pelham (NY); On-site; USD 114,000 - 162,000; Full time

Job summary

Landing Point is seeking an AI Platform Engineer in New York to advance enterprise AI capability, moving from vendor-led to internal operations. You will build reusable AI foundations, RAG pipelines, embeddings services, and memory/prompt systems for compliant, scalable AI products.

You’ll work closely with backend teams to instrument production-ready pipelines on AWS, implement telemetry, and ensure secure, repeatable deployments across knowledge sources and agents.

Qualifications

• 7+ years of software/platform engineering with AI/ML or data-intensive systems.
• Experience with RAG, vector search, embeddings, and retrieval pipeline design.
• Proficient in Python and production systems on AWS/Azure/GCP.

Responsibilities

• Build and mature shared engineering foundations for AI products.
• Develop Retrieval-Augmented Generation (RAG) pipelines as reusable services.
• Create a centralized embeddings service and vector index management.
• Establish a memory bank for persistent, identity-scoped memory.
• Design context assembly logic for reusable prompts.
• Implement content ingestion and processing pipelines for knowledge sources.
• Develop prompt and configuration management systems for versioning.
• Create a shared PII detection and redaction library.
• Build a platform SDK and golden-path templates for compliant deployment.
• Integrate evaluation hooks for quality checks in AI products.
• Implement telemetry for usage, performance, and quality signals.
• Align identity and access integration with enterprise standards.
• Standardize deployment patterns and support practices for AI products.

Skills

RAG design

Vector search

Embeddings

CI/CD

Python

AWS

Production services

APIs

Observability

Memory management

Tools

Python

CI/CD

Vector databases

AWS

Job description

Company Overview

A leading financial services company is seeking an AI Platform Engineer to enhance its AI capabilities. This role focuses on building reusable enterprise AI solutions, transitioning from vendor-led to internal team operations.

Job Responsibilities

• Build and mature shared engineering foundations for AI products.


• Develop Retrieval-Augmented Generation (RAG) and retrieval pipelines as reusable services.


• Create a centralized embeddings service and vector index management.


• Establish a memory bank capability for persistent, identity-scoped agent/assistant memory.


• Design context assembly logic for consistent, reusable prompts.


• Implement content ingestion and processing pipelines for enterprise knowledge sources.


• Develop prompt and configuration management systems for versioning and reuse.


• Create a shared PII detection and redaction library.


• Build a platform SDK and golden-path templates for compliant AI product deployment.


• Integrate evaluation hooks for quality checks in AI products.


• Implement telemetry integration for usage, performance, and quality signals.


• Align identity and access integration with enterprise standards.


• Standardize deployment patterns and support practices for AI products.

Qualifications

• 7+ years of experience in software or platform engineering, with recent work in AI/ML or data-intensive systems.


• Practical experience with RAG, vector search, embeddings, and retrieval pipeline design.


• Familiarity with agent memory, context management, and PII-handling patterns.


• Strong software engineering fundamentals: APIs, testing, CI/CD, and observability.


• Experience with prompt/configuration management and integrating LLMs into production systems.


• Proficiency in Python and/or another primary backend language.


• Fluent in AWS, with familiarity with other major cloud platforms (Azure, GCP).


• Experience operating and supporting production services.

Compensation

Pay Rate: $100 - $100/hr, DOE