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Home Based Llm Engineer information
What is the difference between Home Based Llm Engineer vs Data Scientist?
| Aspect | Home Based Llm Engineer | Data Scientist |
|---|---|---|
| Required Credentials | Degree in Computer Science, AI, or related fields; experience with machine learning models | Degree in Data Science, Statistics, or related fields; proficiency in programming and analytics |
| Work Environment | Remote, focused on developing and fine-tuning language models | Remote or on-site, analyzing data and building predictive models |
| Industry Usage | AI companies, tech firms, research institutions | Tech, finance, healthcare, and various industries requiring data analysis |
Home Based Llm Engineers focus on developing and optimizing large language models remotely, while Data Scientists analyze data to generate insights. Both roles require strong technical skills and often work in similar industries, but their core responsibilities differ in model development versus data analysis.
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The most popular types of Llm Engineer jobs are:
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States with the most job openings for Home Based Llm Engineer jobs include:
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AI/LLM Engineer
Harrison, NJ • On-site
2T Consulting •
IT Services • 51 - 200 employees51 - 200 employees
Full-time
This job post has expired 1 day ago. Applications are no longer accepted.
Job description
We are seeking a highly skilled AI/LLM Engineer to design, develop, and deploy intelligent agent-based systems capable of reasoning, planning, and executing tasks autonomously. The ideal candidate will have strong Python engineering skills, hands-on experience with modern AI agent frameworks, and experience integrating LLM solutions with enterprise data platforms.
Roles and Responsibilities
Agent & AI System Development
- Design and develop AI agent systems using LangChain, LangGraph, or similar frameworks.
- Build intelligent components capable of reasoning, planning, and autonomous task execution.
- Implement agentic patterns such as ReAct (Reasoning + Acting).
- Develop multi-step LLM workflows and agent orchestration.
LLM Capabilities & Enhancements
- Implement memory, tool usage, context management, and MCP capabilities.
- Develop solutions using tool calling, chaining, and workflow orchestration.
- Work with prompt engineering teams to improve LLM response quality and performance.
- Optimize LLM applications for scalability, reliability, and cost.
Software Engineering
- Develop high-quality, scalable, and maintainable Python applications, including asynchronous programming.
- Build and maintain APIs and microservices for AI and agent-based applications.
- Integrate AI services with enterprise applications and backend systems.
- Develop production-ready solutions with appropriate testing, monitoring, and error handling.
Guardrails & Responsible AI
- Design and implement guardrails and safety mechanisms for LLM-powered applications.
- Handle edge cases, failures, hallucinations, and unintended model outputs.
- Implement appropriate validation, security, and access controls for AI systems.
Data & Platform Integration
- Integrate AI solutions with modern data platforms, including Snowflake and Databricks.
- Work with Lakehouse architectures and enterprise data pipelines.
- Enable LLM applications to securely access and leverage enterprise data.
- Collaborate with Data Engineering, ML Engineering, and Cloud teams on scalable AI architectures.
Required Technical Skills
- 5+ years of experience in software engineering, AI/ML engineering, or a related field.
- Strong hands-on experience with Python and asynchronous programming.
- Experience building LLM and generative AI applications.
- Strong experience with LangChain, LangGraph, or similar agent frameworks.
- Understanding of AI agents, ReAct, tool calling, memory, context management, and MCP.
- Experience developing REST APIs and microservices.
- Experience integrating AI applications with Snowflake, Databricks, or Lakehouse platforms.
- Strong understanding of scalable, production-ready software architectures.
- Knowledge of LLM guardrails, responsible AI, security, and failure handling.
- Strong problem-solving, communication, and collaboration skills.
Preferred Skills
- Experience with multi-agent systems and agent orchestration.
- Knowledge of vector databases and RAG architectures.
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Familiarity with LLM observability, evaluation, and performance optimization.
- Experience deploying AI applications in enterprise production environments.
About 2T Consulting
Sourced by ZipRecruiter
Industry
It services
Company size
51 - 200 Employees
Headquarters location
Edison, NJ, US