Mindlance
AI ML Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI ML Engineer with a contract length of "X months" and a pay rate of "$X/hour." Required skills include Python, LLM integration (LangChain, LangGraph), and experience with model fine-tuning and prompt engineering.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
October 17, 2025
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Irving, TX
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🧠 - Skills detailed
#Datasets #Langchain #AI (Artificial Intelligence) #Python #Libraries #Databases #Transformers #"ETL (Extract #Transform #Load)" #Automation #Hugging Face #REST (Representational State Transfer) #ML (Machine Learning)
Role description
Job Description:
Summary:
AI Engineer / LLM Engineer / Generative AI Engineer
This role focuses on building, fine-tuning, and deploying Large Language Model (LLM)-based applications using frameworks like LangChain and LangGraph.
Key Responsibilities:
1. Prompt Engineering / Optimization
o Crafting and refining prompts and context windows to improve model accuracy, relevance, and consistency.
o Experimenting with few-shot and chain-of-thought prompting.
o Managing token usage and response quality trade-offs.
1. LLM Integration (LangChain / LangGraph)
o Developing pipelines and agent frameworks that orchestrate LLM calls.
o Building modular components like retrievers, memory, and custom tools.
o Integrating with vector databases (e.g., FAISS, Chroma, Pinecone) for retrieval-augmented generation (RAG).
1. Model Fine-tuning / Transfer Learning
o Using pre-trained models (e.g., GPT, LLaMA, Mistral, Falcon, etc.) and adapting them to domain-specific datasets.
o Training with libraries such as Hugging Face Transformers, PEFT, or LoRA.
o Evaluating model performance and running inference benchmarks.
1. Python Development
o Writing efficient Python code for model orchestration, data preprocessing, and pipeline automation.
o Working with APIs, REST endpoints, or SDKs to integrate LLM outputs into applications.
EEO:
Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.
Job Description:
Summary:
AI Engineer / LLM Engineer / Generative AI Engineer
This role focuses on building, fine-tuning, and deploying Large Language Model (LLM)-based applications using frameworks like LangChain and LangGraph.
Key Responsibilities:
1. Prompt Engineering / Optimization
o Crafting and refining prompts and context windows to improve model accuracy, relevance, and consistency.
o Experimenting with few-shot and chain-of-thought prompting.
o Managing token usage and response quality trade-offs.
1. LLM Integration (LangChain / LangGraph)
o Developing pipelines and agent frameworks that orchestrate LLM calls.
o Building modular components like retrievers, memory, and custom tools.
o Integrating with vector databases (e.g., FAISS, Chroma, Pinecone) for retrieval-augmented generation (RAG).
1. Model Fine-tuning / Transfer Learning
o Using pre-trained models (e.g., GPT, LLaMA, Mistral, Falcon, etc.) and adapting them to domain-specific datasets.
o Training with libraries such as Hugging Face Transformers, PEFT, or LoRA.
o Evaluating model performance and running inference benchmarks.
1. Python Development
o Writing efficient Python code for model orchestration, data preprocessing, and pipeline automation.
o Working with APIs, REST endpoints, or SDKs to integrate LLM outputs into applications.
EEO:
Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.
