KTek Resourcing
AI/ML and Analytics
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI/ML and Analytics expert in Woodland Hills, CA, requiring 10+ years of experience. Key skills include ML concepts, GenAI/LLMs, evaluation frameworks, and proficiency in Python, SQL, and ML lifecycle tools.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
April 7, 2026
🕒 - Duration
Unknown
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Los Angeles Metropolitan Area
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🧠 - Skills detailed
#"ETL (Extract #Transform #Load)" #Classification #Supervised Learning #Unsupervised Learning #ML (Machine Learning) #MLflow #AI (Artificial Intelligence) #Python #SQL (Structured Query Language) #Monitoring
Role description
Job Title: AI/ML and Analytics
Location: Woodland Hills, CA (Onsite)
10+ years Experience Required
AI/ML and analytics qualifications
• Strong understanding of ML concepts: supervised/unsupervised learning, time-to-event, uplift, calibration, bias/variance, drift, monitoring.
• Experience with GenAI/LLMs and traditional ML where appropriate:
• Document understanding (prior auth packets, EOBs), summarization, classification, entity extraction
• Retrieval-Augmented Generation (RAG) design, prompt/response evaluation, guardrails
• Ability to design evaluation frameworks:
• Offline metrics (precision/recall, AUROC, MAE), business KPIs, human-in-the-loop QA
• LLM evals (grounded Ness, hallucination rate, toxicity, PHI leakage checks)
• Working knowledge of common toolchains (examples): Python, SQL, notebooks, ML lifecycle tooling (MLflow or equivalent).
Job Title: AI/ML and Analytics
Location: Woodland Hills, CA (Onsite)
10+ years Experience Required
AI/ML and analytics qualifications
• Strong understanding of ML concepts: supervised/unsupervised learning, time-to-event, uplift, calibration, bias/variance, drift, monitoring.
• Experience with GenAI/LLMs and traditional ML where appropriate:
• Document understanding (prior auth packets, EOBs), summarization, classification, entity extraction
• Retrieval-Augmented Generation (RAG) design, prompt/response evaluation, guardrails
• Ability to design evaluation frameworks:
• Offline metrics (precision/recall, AUROC, MAE), business KPIs, human-in-the-loop QA
• LLM evals (grounded Ness, hallucination rate, toxicity, PHI leakage checks)
• Working knowledge of common toolchains (examples): Python, SQL, notebooks, ML lifecycle tooling (MLflow or equivalent).
