2

Vector Remote Jobs in Oklahoma (NOW HIRING)

Location: Remote (U.S. or Canada) Type: US Applicants - Full-Time; Canadian Applicants ... You'll build and run red team exercises against our AI systems, model attack vectors that don't yet ...

Vector Remote information

What are some of the unique challenges faced when working as a remote sales representative for Vector Marketing?

As a remote sales representative for Vector Marketing, one of the main challenges is maintaining self-motivation and discipline, since you are often managing your own schedule without direct in-person supervision. Additionally, building rapport with customers virtually requires strong communication skills and adaptability to different digital platforms. You may also need to proactively seek feedback and support from your team, as remote work can sometimes feel isolating. However, the company offers regular virtual meetings, training, and resources to help you stay connected and succeed in your sales goals.

What are the key skills and qualifications needed to thrive as a Vector Remote (Remote Sales Representative at Vector Marketing), and why are they important?

To thrive as a Vector Remote Sales Representative, you need strong sales acumen, effective communication skills, and a high school diploma or equivalent. Familiarity with CRM software, virtual meeting platforms, and online order processing systems is typically required. Exceptional interpersonal skills, self-motivation, and resilience help individuals stand out in this role. These skills are crucial for building client relationships, meeting sales targets, and succeeding in a remote work environment.

What are Vector Remote jobs?

Vector Remote jobs refer to employment opportunities offered by Vector Marketing that allow representatives to work remotely, typically selling CUTCO® products from home or any location with internet access. These positions often involve conducting virtual demonstrations, managing customer relationships online, and setting appointments through digital platforms. Remote roles offer flexible scheduling, making them attractive to students and those seeking supplemental income. Training and support are usually provided virtually, ensuring remote reps have the resources needed to succeed. This setup allows individuals to gain sales and communication experience without needing to work in a traditional office environment.

What is the difference between Vector Remote vs Data Analyst?

AspectVector RemoteData Analyst
Required CredentialsBachelor's degree in relevant field, technical skills in data toolsBachelor's degree in statistics, mathematics, or related field
Work EnvironmentRemote, flexible hours, tech-focusedTypically office-based or remote, analytical and reporting tasks
Employer & Industry UsageTech companies, startups, remote-first organizationsBusiness, finance, healthcare, and various industries
Search & Comparison IntentRemote data roles, tech jobs, data-focused positionsData analysis careers, analytics roles, data reporting jobs

Vector Remote and Data Analyst roles often require similar technical skills and educational backgrounds. While Vector Remote emphasizes remote work environments and tech industry usage, Data Analysts are found across multiple industries with both remote and on-site options. Understanding these differences helps job seekers target the right roles based on work setting and industry focus.

Infographic showing various Vector Remote job openings in Oklahoma as of July 2026, with employment types broken down into 88% Full Time, 8% Part Time, and 4% Contract. Highlights an 51% Physical, 4% Hybrid, and 45% Remote job distribution.

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Oklahoma City, OK • Remote

$99K - $130K/yr

Full-time

Posted 12 days ago


Job description

Senior Software Engineer: Applied AI (Voice Agents & ML Systems)

AMC Health · Remote (US) · Full-time

The pitch

We build and operate production AI voice agents that hold real phone conversations in a regulated healthcare setting, plus the machine learning and LLM pipelines around them. This is one seat that spans four disciplines that rarely come together: real-time systems, LLM engineering, traditional machine learning, and serious cloud infrastructure, all in production, all with real consequences. If you are the kind of engineer who gets restless doing one thing, this role is the opposite problem.

What you'll work across

Real-time voice AI

  • Streaming, low-latency speech-to-speech systems built on modern LLMs
  • Telephony and real-time media (call control, live audio streaming)
  • Audio handling and the quirks of real human conversation (interruptions, timing, noise)
  • Concurrency on a latency-sensitive path, where p99 matters and a stall is something a caller hears

LLM engineering

  • Wrapping nondeterministic models in deterministic control so they behave reliably in production
  • Multi-model pipelines, prompt design, and cost/latency budgeting
  • Evaluation harnesses, including LLM-as-judge and automated agent-tests-agent approaches
  • Agentic tooling that gives AI systems safe, structured access to infrastructure

Traditional (non-LLM) machine learning

  • End-to-end ML pipelines: feature engineering, model training, and scheduled inference
  • Imbalanced, messy real-world data; calibration and explainability for non-technical consumers
  • Turning research notebooks into reproducible, auditable production pipelines

Cloud and infrastructure

  • Infrastructure as code across multiple environments (we run on AWS)
  • Managed compute, data, streaming, and orchestration services
  • Security engineering in a regulated setting: encryption, least-privilege access, strict data-handling discipline
  • Observability and telemetry-driven debugging, tracing a production issue from a metric anomaly to root cause

Plus occasional full-stack work on internal tools, and an engineering workflow that leans heavily on AI coding assistants, with human accountability for every change.

What you'll actually do

  • Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
  • Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
  • Build and operate LLM evaluation and batch-analysis pipelines
  • Own traditional ML workflows from data to scheduled production inference
  • Trace production issues from a metric anomaly to root cause, including building the evidence when the cause is a vendor

Must-haves

  • 7+ years building and operating production backend systems, with strong general-purpose programming skills (we work primarily in Python)
  • Experience running distributed systems in the cloud; comfortable debugging from telemetry to root cause
  • Hands-on production experience with LLMs or generative AI (any provider or framework), plus the judgment to know when not to use a model
  • Working fluency across the traditional machine learning lifecycle (you productionize; you do not need to publish)
  • Disciplined in a regulated environment: small, reviewable changes and careful handling of sensitive data

Nice-to-haves

  • Real-time media or telephony experience
  • Front-end / full-stack ability
  • ML pipeline experience, vector search, or embeddings
  • Fluency with AI coding assistants (our workflows assume them, with human accountability for every change)

How we work

Smallest correct change wins. Every behavior change is validated against the live system. Evidence over opinion in debugging. Code review is rigorous. Safety and privacy gate everything.

Work authorization (no exceptions)

This role is open only to US citizens and lawful permanent residents (Green Card holders). We cannot consider candidates who require visa sponsorship now or in the future, and we are unable to make exceptions of any kind.

How to apply

Please submit both of the following:

  • Your LinkedIn profile URL
  • A phone number where we can reach you

A resume is welcome but optional; the two items above are required.