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Remote Prompt Engineering Jobs in Orlando, FL (NOW HIRING)

Senior Agentic (AI) Engineer

Orlando, FL · On-site +1

$97K - $134K/yr

... prompt injection, PII leakage, or unsafe tool use on agents you own. * Force Multiplier: Patterns, tools, and eval scaffolding you build get adopted across engineering. All Remote Hires will be ...

Senior Agentic (AI) Engineer

Orlando, FL · Remote

$107K - $146K/yr

... prompt injection, PII leakage, or unsafe tool use on agents you own. * Force Multiplier: Patterns, tools, and eval scaffolding you build get adopted across engineering. All Remote Hires will be ...

Senior Agentic (AI) Engineer

Orlando, FL · On-site +1

$97K - $134K/yr

... prompt injection, PII leakage, or unsafe tool use on agents you own. * Force Multiplier: Patterns, tools, and eval scaffolding you build get adopted across engineering. All Remote Hires will be ...

Remote Prompt Engineering information

See Orlando, FL salary details

$16

$30

$44

How much do remote prompt engineering jobs pay per hour?

As of Aug 13, 2026, the average hourly pay for remote prompt engineering in Orlando, FL is $30.86, according to ZipRecruiter salary data. Most workers in this role earn between $24.66 and $35.67 per hour, depending on experience, location, and employer.

What is the difference between Remote Prompt Engineering vs Remote Data Annotation Specialist?

AspectRemote Prompt EngineeringRemote Data Annotation Specialist
Required CredentialsBasic understanding of AI, NLP, and scripting skillsAttention to detail, familiarity with annotation tools, no formal certifications required
Work EnvironmentCollaborative with AI/ML teams, remote setupIndependent annotation tasks, remote or on-site
Industry UsageAI development, NLP projects, machine learningData labeling for AI training datasets
Search & Comparison IntentUnderstanding roles in AI development, job requirementsData labeling jobs, annotation tasks, related roles

Remote Prompt Engineering involves designing and refining prompts for AI models, requiring some technical skills and collaboration with AI teams. In contrast, Remote Data Annotation Specialists focus on labeling data to train AI systems, emphasizing attention to detail. Both roles are essential in AI development but differ in skills and daily tasks.

What are some common challenges faced by remote prompt engineers, and how can they be addressed?

Remote prompt engineers often face challenges related to communication and collaboration, especially when working across time zones and with interdisciplinary teams. Staying updated on rapidly evolving AI technologies and understanding nuanced user requirements can also be demanding. To address these, prompt engineers can leverage collaborative tools, maintain clear documentation, and participate in regular team syncs. Building a habit of continuous learning and engaging in knowledge-sharing sessions helps keep skills relevant and fosters a sense of connection despite remote work.

What are the key skills and qualifications needed to thrive as a remote prompt engineer?

To thrive as a Remote Prompt Engineer, you need a strong background in natural language processing, programming (often Python), and an understanding of AI/ML concepts, typically supported by a relevant degree or industry experience. Familiarity with large language models (like OpenAI's GPT), prompt optimization tools, and version control systems such as Git is common. Creativity, problem-solving, and strong written communication are vital soft skills for designing effective prompts and collaborating remotely. These skills ensure the development of high-performing AI solutions and seamless teamwork in distributed environments.

What is remote prompt engineering?

Remote prompt engineering is the practice of designing and refining prompts for AI language models, such as ChatGPT, while working from a remote location. Prompt engineers craft instructions or questions to optimize the model’s responses for specific tasks or applications. This role typically involves understanding both the capabilities and limitations of AI systems, as well as the needs of end users or clients. Remote prompt engineers collaborate online with teams and may work for tech companies, research organizations, or as independent contractors.

Are remote prompt engineers still in demand?

Remote prompt engineers are currently in demand as organizations seek expertise in designing effective prompts for AI models. The role often requires skills in natural language processing, AI tools, and continuous learning to keep up with evolving technologies. Demand is driven by the growth of AI applications across various industries.

What are popular job titles related to Remote Prompt Engineering jobs in Orlando, FL?

For Remote Prompt Engineering jobs in Orlando, FL, the most frequently searched job titles are:

What cities near Orlando, FL are hiring for Remote Prompt Engineering jobs?

Cities near Orlando, FL with the most Remote Prompt Engineering job openings:

Infographic showing various Remote Prompt Engineering job openings in Orlando, FL as of August 2026, with employment types broken down into 85% Full Time, 10% Part Time, 4% Contract, and 1% Nights. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution, with an average salary of $64,189 per year, or $30.9 per hour.

Senior Agentic (AI) Engineer

Worth AI

Orlando, FL • On-site, Remote

$97K - $134K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 16 hours ago


Job description

Worth AI is hiring a Senior Agentic AI Engineer to design and ship production agent systems that automate KYB, underwriting, and risk decisions on regulated financial data. You'll own agents end-to-end architecture, retrieval, tools, evals, and production deployment and partner closely with our Chief AI Officer, applied scientists, and platform teams.

Responsibilities
  • Design and ship multi-step agentic systems (planner/executor, tool-using, multi-agent, human-in-the-loop) for onboarding, underwriting, case review, and continuous monitoring.
  • Architect agent graphs in LangGraph (or comparable - CrewAI, AutoGen, Claude Agent SDK) with explicit state, durable execution, retries, and safe fallbacks.
  • Build the retrieval layer powering our agents - chunking, hybrid search, reranking, and grounded citation.
  • Own the eval stack: golden sets, offline regression suites, LLM-as-judge, online A/B and shadow evals, and red-teaming for jailbreaks, prompt injection, and PII leakage.
  • Expose agents to production systems via well-typed tools and MCP servers. Treat tool surface area as a product.
  • Drive production MLOps: deployment, versioning, traffic shaping, cost/latency budgets, tracing, and on-call playbooks for agent incidents.
  • Partner with security and compliance to keep agents inside SOC 2, GDPR, CCPA, and fair-lending posture - auditability and explainability built in, not bolted on.
  • Mentor engineers on agent patterns, prompt hygiene, eval discipline, and LLM failure modes.
  • Technology Stack
    • Languages: Python, Node.js, TypeScript
    • Agent / LLM frameworks: LangGraph, LangChain, Claude Agent SDK, MCP, OpenAI SDK
    • Models: Anthropic Claude, OpenAI, open-weight where appropriate
    • Retrieval & Data: PostgreSQL, pgvector, OpenSearch, Kafka, Redshift, Redis
    • Infra: AWS, Kubernetes (EKS), ArgoCD, Terraform
    • Evals & Observability: LangSmith / Langfuse / Braintrust-style tooling, DataDog

Requirements

  • 5+ years of software engineering experience, with 2+ years building production LLM or agentic systems (not just notebooks or demos).
  • Hands-on experience with a modern agent framework (LangGraph strongly preferred) and a track record of shipping agents that run, fail gracefully, and recover.
  • Strong RAG fundamentals chunking, embeddings, hybrid retrieval, reranking, grounding - and judgment about when RAG isn't the right answer.
  • Real eval experience golden sets, offline and online evaluations, used to make ship/no-ship calls.
  • Production MLOps fluency: deployed LLM workloads under real latency, cost, and reliability constraints.
  • Strong Python; comfortable in TypeScript / Node.js.
  • Solid systems engineering instincts APIs, async patterns, queues, databases, distributed system failure modes.
  • Calibrated communicator; thrives in ambiguous, fast-moving environments.
  • Prior experience in fintech, lending, payments, KYB/KYC, fraud, or AML.
  • Experience building MCP servers or other structured tool interfaces for LLMs.
  • Background in classical ML (ranking, scoring, calibration).
  • Experience designing explainable / auditable AI workflows for regulated environments.
  • Open-source contributions to agent frameworks, eval tooling, or retrieval libraries.
  • AWS depth (EKS, MSK, RDS, S3, Lambda) and IaC with Terraform.
Success Metrics
  • Agent Quality: Measurable improvements in task success rate, grounding accuracy, and hallucination rate on our eval suites.
  • Production Reliability: Agents you own meet defined SLOs for latency (P90/P99), tool-call success, and cost per task.
  • Velocity: New agent capabilities go from prototype to production in weeks, without skipping evals or guardrails.
  • Risk Posture: Zero material incidents tied to prompt injection, PII leakage, or unsafe tool use on agents you own.
  • Force Multiplier: Patterns, tools, and eval scaffolding you build get adopted across engineering.

All Remote Hires will be required to travel to Orlando, Florida at least twice per year for Town Halls and team collaboration, in addition to orientation in Orlando.

Benefits

  • Health Care Plan (Medical, Dental & Vision)
  • Retirement Plan (401k, IRA)
  • Life Insurance
  • Flexible Paid Time Off
  • 9 paid Holidays
  • Family Leave
  • Remote
  • Hybrid work (for Orlando Associates)
  • Free Food & Snacks (Orlando)
  • Wellness Resources