AI/ML Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI/ML Engineer with a 6-month remote contract, offering a pay rate of "unknown." Requires 6+ years of experience, including 4+ years in AI/ML, telecom experience, and proficiency in Python, ML, and NLP.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
-
🗓️ - Date discovered
June 4, 2025
🕒 - Project duration
More than 6 months
-
🏝️ - Location type
Remote
-
📄 - Contract type
Unknown
-
🔒 - Security clearance
Unknown
-
📍 - Location detailed
United States
-
🧠 - Skills detailed
#AWS (Amazon Web Services) #Model Deployment #Python #Cloud #AI (Artificial Intelligence) #Azure #Data Analysis #GCP (Google Cloud Platform) #API (Application Programming Interface) #Kubernetes #Libraries #NLP (Natural Language Processing) #Deployment #ML (Machine Learning) #Databases #Monitoring
Role description
Position: AI/ML Engineer
Location: Remote
Dur: 6 months
Communication (telecom exp must)
6+ years overall experience, including 4+ years in AI/ML
LinkedIn: yes
Hand clicked Visa copy and 2 references required for submission.
Description
Proficiency in Python, ML, NLP, and text data analysis
Hands-on experience with API creation, RAG, Vector databases, LLM prompt engineering, LLM evaluation
Familiarity With MLOps Tools And Practices, Including
• Monitoring and evaluation tools like LangSmith
• LLM deployment libraries like LiteLLM, LLM Guardrails, and more
• CI/CD, model versioning, and model deployment best practices
Knowledge of or willingness to learn Kubernetes and cloud services (AWS/GCP/Azure)
Position: AI/ML Engineer
Location: Remote
Dur: 6 months
Communication (telecom exp must)
6+ years overall experience, including 4+ years in AI/ML
LinkedIn: yes
Hand clicked Visa copy and 2 references required for submission.
Description
Proficiency in Python, ML, NLP, and text data analysis
Hands-on experience with API creation, RAG, Vector databases, LLM prompt engineering, LLM evaluation
Familiarity With MLOps Tools And Practices, Including
• Monitoring and evaluation tools like LangSmith
• LLM deployment libraries like LiteLLM, LLM Guardrails, and more
• CI/CD, model versioning, and model deployment best practices
Knowledge of or willingness to learn Kubernetes and cloud services (AWS/GCP/Azure)
