Senior AI Engineer

Intone Networks Inc.; Bellevue (WA); Hybrid; USD 180,000 - 230,000; Full time

Job summary

Intone Networks Inc. seeks a Senior AI Engineer for a 6-month contract in Bellevue, WA, with a hybrid work arrangement.

You will lead end-to-end GenAI and ML initiatives, from data engineering to deployment, leveraging OpenAI, Anthropic, Azure OpenAI, and in-house models, while coordinating with cross-functional teams. The role requires strong Python expertise, experience with Databricks and Snowflake, and a track record of shipping AI/ML models to production.

Qualifications

• 5–8 years of AI/ML engineering or data science experience with production models.
• Proficiency in Python with pandas/NumPy and standard ML libraries.
• Experience with Databricks (PySpark, Delta Lake, MLflow, Unity Catalog) and Snowflake (SQL, Snowpark, Cortex).
• Hands-on with traditional ML: feature engineering, modeling, evaluation, tuning.
• Experience building generative AI apps (RAG, agents, fine-tuning) using LangChain or similar.

Responsibilities

• Design and build generative AI applications including RAG pipelines and LLM-powered assistants.
• Train and productionize traditional ML models (classification, forecasting, recommendation).
• Develop scalable data pipelines in Databricks and Snowflake environments.
• Own ML/LLMOps lifecycle: versioning, CI/CD, monitoring and retraining.
• Evaluate vector databases and embedding strategies for retrieval-augmented systems.
• Write production-grade Python code and ensure test coverage.
• Apply prompt engineering and evaluation frameworks to improve LLM outputs.
• Collaborate with stakeholders to translate problems into technical solutions.
• Stay updated on GenAI tools and techniques for applicable use cases.

Skills

Python

Pandas/NumPy

Databricks PySpark

Snowflake

CI/CD/MLOps

LLM/GenAI

Vector databases

Cloud platforms

Communication

Tools

Databricks

Snowflake

LangChain

LlamaIndex

Job description

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)