1062 Ai Application Engineer jobs in New York,NY

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AI Systems Lab- Test Engineering Technician

Advanced Micro Devices

Secaucus, NJ 07094

~ 18 min Onsite

Hands on experience testing and troubleshooting semiconductor GPUs, switches and CPUs in a semiconductor lab environment or reliability testing facility.
Previous experience working on server racks and datacenter.
Experience with MS Excel and PowerPoint
Demonstrated effective and collaborative communication and teamwork experience
Experience and ability to work autonomously and also collaboratively within a team environment to support Lab teams, Engineering, NPI, and Services
Work with lab tools (e.g.: diagnostics, tests, and standard semiconductor industry hardware and software)
Experience with Windows Server OS
Experience with Linux PXE services a plus
High attention to detail, and commitment to safety, quality, and continuous improvement
Associate's degree in Electronics, Information Technology or related, or an or equivalent combination of education and experience is preferred

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AI Engineer

Capital One

New York, NY 10001

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Technology Test Engineer

LMI Consulting, LLC

New York, NY 10001

New, Posted 1 day ago

Remote Online Tasks – Earn Up to $1,500/month Playing Games

Free Cash

New York, NY 10170

$1,500/mo

Remote

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Principal Engineer - Python API Development

Fidelity Investments

Hoboken, NJ 07030

~ 16 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's or Master's degree in Computer Science, Software Engineering, or a closely related engineering discipline
8+ years (typically 10+) building and operating production platforms and services at scale
Deep software engineering expertise in Python and distributed systems
A track record of building production‑grade services, libraries, and internal platforms
Linux fluency and scripting are required
Cloud platform leadership (AWS) —hands-on with S3, Lambda, Batch, Step Functions, EventBridge, CloudWatch, and SNS/SQS—and experience shaping platform patterns that other teams adopt
Experience enabling managed ML services (e.g., SageMaker) as part of broader platform capabilities; exposure to Azure or GCP is beneficial
DevOps and CI/CD at scale, owning standards for automated build/test/deploy (e.g., Jenkins, Git‑based workflows), containerization (Docker), release governance, and multi‑environment promotion for ML‑enabled workloads
Infrastructure as Code (CloudFormation, Terraform/OpenTofu) and platform reliability engineering (SLOs/error budgets, capacity planning, cost observability, incident response, and post‑mortems) for ML serving and data/feature pipelines
ML enablement in production: model packaging, deployment strategies (batch/online/streaming), inference routing, traffic management, performance tuning, observability, and controls for responsible use—without a research or modeling focus
Cross‑org technical leadership: you mentor junior and senior engineers, are a backbone of code review across repos, and routinely consider impacts on upstream/downstream systems when proposing changes
Set platform strategy and standards for ML packaging, deployment, serving, and observability—driving consistent adoption across squads and business units
Partner with Data Scientists to package, scale, and operationalize models; define the APIs, guardrails, and automation that take work from experimentation to reliable production
Enable secure, scalable access to traditional and generative models by collaborating with platform and application engineers to integrate through enterprise gateways and services
Advance model/data observability—tooling for data and feature drift detection, prediction‑quality monitoring and uncertainty signals, and automated diagnostics/ explainability
Lead cross‑platform incident response and post‑mortems, drive systemic fixes, and evolve standards to prevent recurrence—across applications and the platform
Uplevel engineering velocity by introducing reusable frameworks, paved paths, and CI/CD templates that simplify integration, reduce toil, and improve reliability at scale
Reduce cost and complexity across the ML ecosystem through pragmatic technology choices, clear abstractions, and a long‑term platform roadmap
The base salary range for this position is $107,000-216,000 USD per year.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.