TechDoQuest
Machine Learning Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer on a contract basis, focusing on RAG pipelines and AI systems. Requires strong LLM experience, Python, Kubernetes, and vector databases. Pay rate is "X" and location is "remote" or "on-site."
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
June 2, 2026
🕒 - 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
Cupertino, CA
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🧠 - Skills detailed
#Deployment #Automation #Python #Databases #Documentation #Kubernetes #Cloud #AI (Artificial Intelligence) #Langchain #ML (Machine Learning) #API (Application Programming Interface) #Docker #Scala
Role description
Build and optimize RAG pipelines, vector databases, embeddings, and document-processing workflows
Design agentic AI systems — including tool calling, orchestration, reasoning loops, and workflow automation
Develop Applied AI solutions that integrate with customer backend systems, APIs, and data sources
Implement AI services on Kubernetes, cloud environments, or customer-controlled infrastructure
Work directly with customers to deliver high-quality technical implementations, demos, and documentation
(Good to have) Integrate AI workflows with PLM systems or enterprise knowledge platforms
REQUIRED SKILLS
Strong experience with LLMs and agent frameworks such as LangChain, LlamaIndex, or custom-built solutions
Expertise in Python, vector databases (Elastic, Milvus, Pinecone, etc.), embeddings, and chunking strategies
Hands-on experience with Kubernetes, Docker, and scalable API deployment
Ability to understand customer workflows and translate them into actionable technical solutions
Build and optimize RAG pipelines, vector databases, embeddings, and document-processing workflows
Design agentic AI systems — including tool calling, orchestration, reasoning loops, and workflow automation
Develop Applied AI solutions that integrate with customer backend systems, APIs, and data sources
Implement AI services on Kubernetes, cloud environments, or customer-controlled infrastructure
Work directly with customers to deliver high-quality technical implementations, demos, and documentation
(Good to have) Integrate AI workflows with PLM systems or enterprise knowledge platforms
REQUIRED SKILLS
Strong experience with LLMs and agent frameworks such as LangChain, LlamaIndex, or custom-built solutions
Expertise in Python, vector databases (Elastic, Milvus, Pinecone, etc.), embeddings, and chunking strategies
Hands-on experience with Kubernetes, Docker, and scalable API deployment
Ability to understand customer workflows and translate them into actionable technical solutions
