Senior AI Engineer

Intone Inc; Bellevue (WA); Híbrido; USD 120 000 - 170 000; Tempo integral

Resumo da oferta

Intone Inc. in Bellevue, WA seeks a Senior AI Engineer for a 6-month contract to lead end-to-end generative AI and ML initiatives. You will design RAG pipelines, build scalable data/feature pipelines in Databricks and Snowflake, and own MLOps practices from versioning to retraining.

The role requires strong Python skills, production ML experience, and collaboration with stakeholders. Hybrid work in Bellevue with cloud and containerization expertise is preferred.

Qualificações

• 5–8 years of AI/ML engineering or data science experience.
• Strong proficiency in Python, including pandas and NumPy.
• Hands-on experience with Databricks (PySpark, Delta Lake, MLflow, Unity Catalog) and Snowflake.
• 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.

Responsabilidades

• Design and build generative AI applications including retrieval-augmented generation (RAG) pipelines, agentic workflows, and LLM-powered assistants.
• Design, train, and productionize traditional machine learning models (classification, regression, forecasting, recommendation, anomaly detection).
• 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.
• 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.

Conhecimentos

Python

AI/ML engineering

Communication

Cloud platforms

CI/CD

Ferramentas

Databricks

Snowflake

MLflow

Unity Catalog

Pinecone

FAISS

Chroma

LangChain

LlamaIndex

LangGraph

CrewAI

AutoGen

Docker

Kubernetes

Descrição da oferta de emprego

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

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)