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Ai Tasker Jobs in Missouri (NOW HIRING)

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AI Software Engineer

Dearborn, MO · On-site

$110 - $150/hr

Develop asynchronous Python (FastAPI/Flask) or Java (Spring Boot) backend services optimized for long-running AI tasks, marketing attribution modeling, and real-time token streaming. * Build ...

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Ai Tasker information

What is the easiest AI Tasker job to get?

The easiest AI Tasker jobs are typically entry-level tasks such as data labeling, content moderation, or simple data entry, which often require minimal experience and can be completed remotely. These roles usually involve following clear instructions and may require basic computer skills or familiarity with AI tools. They are often available through online platforms that connect freelancers with short-term or micro-tasks.

What are the key skills and qualifications needed to thrive as an AI Tasker?

To thrive as an AI Tasker, you need a strong understanding of artificial intelligence concepts, data analysis, and problem-solving abilities, often supported by a background in computer science or a related field. Familiarity with AI platforms, automation tools, and workflow management systems is typically required, along with knowledge of APIs and task management software. Strong communication, attention to detail, and adaptability help AI Taskers excel when collaborating and managing diverse, technology-driven assignments. These capabilities are critical for ensuring accurate execution of AI-powered tasks and effective integration with business processes.

What is an AI Tasker?

An AI Tasker is responsible for training, testing, and refining artificial intelligence models by completing various tasks such as labeling data, reviewing AI-generated content, and providing feedback on model outputs. This role helps improve AI systems by ensuring accuracy and relevance in their responses. AI Taskers often work remotely and require attention to detail, critical thinking skills, and familiarity with AI tools.

What does an AI Tasker do?

As an AI Tasker, your day often involves reviewing and managing a variety of AI-assisted assignments such as data categorization, process automation, or QA testing on digital platforms. You may coordinate with team members to clarify project requirements, set priorities, and troubleshoot technical issues that arise during task execution. Regular collaboration with both AI engineers and project managers helps ensure deliverables meet quality standards and deadlines. The workload can be dynamic, requiring flexibility and proactive communication to handle shifting project demands efficiently.

What are the most commonly searched types of Ai Tasker jobs in Missouri? The most popular types of Ai Tasker jobs in Missouri are:
What are popular job titles related to Ai Tasker jobs in Missouri? For Ai Tasker jobs in Missouri, the most frequently searched job titles are:
What job categories do people searching Ai Tasker jobs in Missouri look for? The top searched job categories for Ai Tasker jobs in Missouri are:
What cities in Missouri are hiring for Ai Tasker jobs? Cities in Missouri with the most Ai Tasker job openings:
Ai Tasker jobs near you
Infographic showing various Ai Tasker job openings in Missouri as of August 2026, with employment types broken down into 76% Full Time, 22% Part Time, and 2% Contract. Highlights an 81% In-person, 2% Hybrid, and 17% Remote job distribution.

AI Software Engineer

Jobtailor

Dearborn, MO • On-site

$110 - $150/hr

Other

Posted 5 days ago

Job description

Responsibilities
  • Design and Build Agentic Workflows: Design and build AI-powered applications, agents, and intelligent workflows that improve discovery, recommendation, and analysis of marketing enterprise analytics assets, customer data, and media metrics.
  • Architect Autonomous Loops: Transition from linear "chain" workflows to self-correcting agentic loops using frameworks like LangChain, LangGraph, or LlamaIndex to automate complex marketing analytics workflows.
  • Implement Tool-Use & MCP: Design and implement robust "tool-calling" capabilities, ensuring LLMs can reliably interact with external marketing APIs, Customer Data Platforms (CDPs), and internal media databases. Build and maintain Model Context Protocol (MCP) servers to bridge the gap between LLMs and our proprietary marketing data silos securely and in real-time.
  • Develop High-Concurrency Backends: Develop asynchronous Python (FastAPI/Flask) or Java (Spring Boot) backend services optimized for long-running AI tasks, marketing attribution modeling, and real-time token streaming.
  • Build Streaming Frontends: Build responsive, stateful UIs in React or Angular that handle complex AI interactions (streaming text, generative UI components, and multi-modal feedback).
  • Optimize Advanced RAG Pipelines: Implement advanced RAG pipelines (re-ranking, query transformation, and embedding optimization) to maximize retrieval precision over vast libraries of marketing assets, creative guidelines, and historical campaign results.
  • Establish AI Evals & Observability: Establish AI Evals to quantify hallucination rates, latency, and cost, leading the shift from "vibes-based" testing to rigorous, automated AI benchmarking.
  • Collaborate & Guide: Collaborate with product managers, marketing experts, and domain stakeholders to translate business needs into technical solutions. Drive architecture decisions, engineering best practices, and operational excellence.
Requirements
  • Masters Degree in Computer Science, Information Systems, or a related quantitative field.
  • 3+ years of professional experience in Software Engineering or Data Science building scalable production systems.
  • 1+ years of hands-on experience designing, training, and deploying complex AI/ML systems in production environments.
  • Experience working with Python, Java, JavaScript, or Angular programming languages.
  • Experience in building autonomous agents using agent orchestration frameworks such as LangGraph, LangChain, LlamaIndex, or similar technologies.
  • Experience implementing tool integration patterns (MCP), agent communication protocols, and AI application observability.
  • Experience with vector search, hybrid retrieval architectures, or vector databases (Chroma, Qdrant, Pinecone, pgvector).
  • Experience working with GCP services (Vertex AI, Cloud Run, and BigQuery) or similar cloud platforms for deploying scalable AI solutions.
  • Strong problem-solving and system design skills with the ability to evaluate competing technical approaches and articulate tradeoffs.
  • Solid understanding of data engineering (SQL, Spark, data pipelines) and a strong interest or experience in quantitative marketing concepts like multi-touch attribution, cohort analysis, and predictive customer lifetime value.
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