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Senior AI Engineer
Intone Inc
1 hour ago
Full-time
On-site
Bellevue, Washington, United States
We are seeking a Senior AI Engineer for a 6-month contract position based in Bellevue, Washington (hybrid). This role offers the opportunity to work across the full lifecycle of generative AI and machine learning systems, from data engineering through model deployment, with expertise spanning both production GenAI applications and traditional ML pipelines.
Responsibilities
Design and build generative AI applications including retrieval-augmented generation (RAG) pipelines, agentic workflows, and LLM-powered assistants, integrating foundation model APIs (OpenAI, Anthropic, Azure OpenAI) and open-source models
Design, train, and productionize traditional machine learning models (classification, regression, forecasting, recommendation, anomaly detection) using scikit-learn, XGBoost, and LightGBM
Build and maintain scalable data and feature engineering pipelines in Databricks (PySpark, Delta Lake, MLflow) and Snowflake (SQL, Snowpark, Cortex)
Own the ML and LLM operations lifecycle, including model versioning, CI/CD, monitoring, drift detection, and retraining
Evaluate and implement vector databases and embedding strategies for retrieval-augmented systems
Write clean, well-tested, production-quality Python code
Apply prompt engineering, fine-tuning, and evaluation frameworks to improve LLM output quality and reduce hallucination
Collaborate cross-functionally to translate business problems into technical solutions; present findings to technical and non-technical stakeholders
Stay current with the generative AI landscape and evaluate new tools, models, and techniques for relevant use cases
Qualifications Required
5–8 years of experience in AI/ML engineering or data science, with a track record of shipping models to production
Strong proficiency in Python, including pandas, NumPy, and standard ML libraries
Hands-on experience with Databricks (PySpark, Delta Lake, MLflow, Unity Catalog) and Snowflake (SQL, Snowpark, Cortex or similar)
Solid grounding in traditional ML: feature engineering, model selection, evaluation metrics, and hyperparameter tuning
Demonstrated experience building generative AI applications (RAG, agents, fine-tuning) using LangChain, LlamaIndex, or comparable frameworks
Experience with vector databases (Pinecone, Weaviate, FAISS, Chroma, or native Snowflake/Databricks vector search)
Familiarity with cloud platforms (AWS, Azure, or GCP) and containerization (Docker, Kubernetes)
Working understanding of MLOps/LLMOps practices: CI/CD, model monitoring, and experiment tracking
Strong communication skills, able to explain technical tradeoffs to non-technical audiences
Preferred
Experience fine-tuning open-source LLMs (Llama, Mistral, etc.) using LoRA/QLoRA
Exposure to multi-agent orchestration frameworks (LangGraph, CrewAI, AutoGen)
Knowledge of data governance and responsible AI practices (bias detection, explainability, guardrails)
Prior consulting or client-facing delivery experience
Relevant certifications (Databricks, Snowflake, or cloud provider ML certifications)
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Responsibilities
Design and build generative AI applications including retrieval-augmented generation (RAG) pipelines, agentic workflows, and LLM-powered assistants, integrating foundation model APIs (OpenAI, Anthropic, Azure OpenAI) and open-source models
Design, train, and productionize traditional machine learning models (classification, regression, forecasting, recommendation, anomaly detection) using scikit-learn, XGBoost, and LightGBM
Build and maintain scalable data and feature engineering pipelines in Databricks (PySpark, Delta Lake, MLflow) and Snowflake (SQL, Snowpark, Cortex)
Own the ML and LLM operations lifecycle, including model versioning, CI/CD, monitoring, drift detection, and retraining
Evaluate and implement vector databases and embedding strategies for retrieval-augmented systems
Write clean, well-tested, production-quality Python code
Apply prompt engineering, fine-tuning, and evaluation frameworks to improve LLM output quality and reduce hallucination
Collaborate cross-functionally to translate business problems into technical solutions; present findings to technical and non-technical stakeholders
Stay current with the generative AI landscape and evaluate new tools, models, and techniques for relevant use cases
Qualifications Required
5–8 years of experience in AI/ML engineering or data science, with a track record of shipping models to production
Strong proficiency in Python, including pandas, NumPy, and standard ML libraries
Hands-on experience with Databricks (PySpark, Delta Lake, MLflow, Unity Catalog) and Snowflake (SQL, Snowpark, Cortex or similar)
Solid grounding in traditional ML: feature engineering, model selection, evaluation metrics, and hyperparameter tuning
Demonstrated experience building generative AI applications (RAG, agents, fine-tuning) using LangChain, LlamaIndex, or comparable frameworks
Experience with vector databases (Pinecone, Weaviate, FAISS, Chroma, or native Snowflake/Databricks vector search)
Familiarity with cloud platforms (AWS, Azure, or GCP) and containerization (Docker, Kubernetes)
Working understanding of MLOps/LLMOps practices: CI/CD, model monitoring, and experiment tracking
Strong communication skills, able to explain technical tradeoffs to non-technical audiences
Preferred
Experience fine-tuning open-source LLMs (Llama, Mistral, etc.) using LoRA/QLoRA
Exposure to multi-agent orchestration frameworks (LangGraph, CrewAI, AutoGen)
Knowledge of data governance and responsible AI practices (bias detection, explainability, guardrails)
Prior consulting or client-facing delivery experience
Relevant certifications (Databricks, Snowflake, or cloud provider ML certifications)
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