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Language Model Jobs in Queens, NY (NOW HIRING)

Experience with Small Language Models (SLM), Agent-to-Agent (A2A) communication, and Model Context Protocol (MCP). * Proven ability to architect and scale AI solutions for enterprise workloads (1M ...

Lead the design, training, fine-tuning, and deployment of large language models, leveraging techniques like prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.

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Language Model information

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$10

$32

$69

How much do language model jobs pay per hour?

As of Sep 7, 2026, the average hourly pay for language model in Queens, NY is $32.73, according to ZipRecruiter salary data. Most workers in this role earn between $19.81 and $40.87 per hour, depending on experience, location, and employer.

What is a language model?

Language models are artificial intelligence systems designed to understand, generate, and manipulate human language. They are trained on vast amounts of text data to predict the next word in a sequence, answer questions, write content, translate languages, and perform other language-related tasks. Modern language models, such as those based on deep learning, have revolutionized natural language processing by enabling more accurate and context-aware interactions between humans and machines.

What are the common challenges faced by professionals working on language model development teams?

Professionals developing language models often encounter challenges such as managing large datasets, addressing biases in training data, and optimizing model performance while balancing computational resources. Collaboration with cross-functional teams—including data scientists, engineers, and domain experts—is essential to ensure the model's accuracy and relevance. Additionally, staying current with rapid advancements in AI research and maintaining responsible AI practices are crucial aspects of the role.

What are the key skills and qualifications needed to thrive as a language model, and why are they important?

To thrive as a Language Model Engineer, you need a strong background in computer science, machine learning, and natural language processing, often supported by a relevant degree. Experience with frameworks like TensorFlow or PyTorch, and familiarity with large-scale data processing tools, are typically required. Strong analytical thinking, collaboration, and problem-solving skills help in designing effective models and working with cross-functional teams. These capabilities are crucial for developing performant and accurate language models that meet complex real-world communication needs.

What is the difference between Language Model vs Data Scientist?

AspectLanguage ModelData Scientist
Required CredentialsNone specific; knowledge of NLP and AI concepts helpfulBachelor's or higher in Data Science, Statistics, or related fields
Work EnvironmentAI development teams, research labs, tech companiesBusiness, finance, healthcare, and various industries
Employer & Industry UsageUsed in AI applications, chatbots, content generationAnalyzing data, building models, providing insights

While both roles involve working with data and AI, a Language Model is an AI system designed to understand and generate human language, often developed by AI engineers. A Data Scientist analyzes data to extract insights and build predictive models, often utilizing language models as tools. Understanding the differences helps clarify career paths and job expectations in the AI and data fields.

What are popular job titles related to Language Model jobs in Queens, NY?

For Language Model jobs in Queens, NY, the most frequently searched job titles are:

What job categories do people searching Language Model jobs in Queens, NY look for?

The top searched job categories for Language Model jobs in Queens, NY are:

What cities near Queens, NY are hiring for Language Model jobs?

Cities near Queens, NY with the most Language Model job openings:

AI Architect - Remote

Saransh Inc

New York, NY • On-site

Contractor

Re-posted 19 days ago


Job description

Title : AI Architect 
Location: NYC, NY
 

AI Architect to lead the design and implementation of enterprise-scale AI solutions for financial services automation. Drive architectural decisions for LLM-based systems, agentic workflows, and intelligent document processing platforms serving private equity and fund management operations.

Required Qualifications:

  • 15+ years of experience in AI/ML architecture with 8+ years in enterprise AI solutions.
  • Deep expertise in LLM architectures, prompt engineering, and agentic frameworks (LangGraph, LangMem).
  • Hands-on experience with Azure OpenAI GPT-4/5, embedding models, and Azure cloud services.
  • Strong background in Python, distributed systems, and enterprise architecture.
  • Experience with Claude Code for agentic coding and AI-powered development.
  • Proven track record in financial services or regulatory compliance environments.
  • Expert knowledge of RAG architectures, advanced RAG patterns, and vector database optimization.
  • Experience with Small Language Models (SLM), Agent-to-Agent (A2A) communication, and Model Context Protocol (MCP).
  • Proven ability to architect and scale AI solutions for enterprise workloads (1M+ documents, sub-second response times).

Key Responsibilities

  • Design end-to-end AI solutions for private equity fund operations and financial automation.
  • Architect scalable agentic AI frameworks using LangGraph, LangMem, and custom agent orchestration.
  • Lead technical strategy for Azure OpenAI GPT-5 integration and advanced embedding-based retrieval systems.
  • Design and implement advanced RAG architectures including hybrid search, query routing, and contextual retrieval.
  • Establish multi-agent systems with Agent-to-Agent (A2A) communication protocols and Model Context Protocol (MCP).
  • Architect Small Language Model (SLM) integration for specialized tasks and cost optimization.
  • Design enterprise-scale solutions supporting millions of documents with sub-second query response times.
  • Establish AI governance, model safety protocols, and regulatory compliance frameworks.
  • Lead architectural reviews for distributed AI systems, microservices, and cloud-native deployments.
  • Hands-on development using Claude Code for rapid prototyping and agentic workflows.
  • Drive architectural reviews for LlamaParse/Azure Document Intelligence integration.
  • Design fault-tolerant, high-availability AI systems with automatic failover and load balancing.
  • Establish comprehensive monitoring, observability, and performance optimization strategies.
  • Mentor technical teams and establish AI engineering best practices using modern toolchains.
Oversee model performance evaluation using LangGraph evals and DeepEval frameworks.