Senior AI Solutions Engineer

Quick Facts

• Role: Senior AI Solutions Engineer

Description

Senior-level AI Solutions Engineer building and deploying production ML and LLM solutions on large-scale datasets and Azure/Snowflake environments. You will integrate AI agents with structured data, implement RAG and document processing pipelines, and deliver end-to-end MLOps with monitoring, testing, and cost/performance optimization. The role also includes technical roadmap ownership and senior engineering responsibilities like PR review and mentoring.

Responsibilities

• Develop production-quality Python code and automated tooling

• Design and implement ML pipelines: feature engineering, training/evaluation, and leakage-safe validation

• Build LLM agents and RAG systems, including tool-use patterns, multi-step orchestration, grounding/citations, and hallucination control

• Integrate LLM/agents with SQL/Snowflake and external APIs/tools

• Implement OCR/document parsing and NLP workflows for large-scale extraction, classification, summarization, and semantic search

• Orchestrate data/ML workflows and manage CDC/orchestration

• Deploy and scale AI/ML workloads on Azure using containers and secure image registries

• Own CI/CD, infrastructure as code, model lifecycle management, and automated testing/validation

• Set up monitoring and alerting for model performance and data drift using Azure observability tools

• Lead engineering standards, review PRs, mentor junior engineers, and communicate with stakeholders

• Troubleshoot production data/AI incidents and support incident management

Requirements

• Strong production programming skills in Python

• Advanced SQL experience with large-volume datasets using Spark and/or Snowflake

• Comfortable scripting in Python and Bash

• Expertise with Git workflows (branching strategies, code review, pull requests)

• Experience with experiment tracking and model/versioning (e.g., MLflow)

• Deep Snowflake knowledge: medallion architecture, advanced SQL patterns, governance/security, and performance/cost optimization

• Hands-on experience building and deploying ML on large-scale production datasets

• Experience with regression, classification, clustering, ensemble methods, and time series forecasting

• Experience with optimization modeling (linear programming, mixed-integer programming)

• LLM application and AI agent development with orchestration frameworks (tool-use patterns, multi-step orchestration)

• RAG pipeline development: chunking, embeddings/vector indexing (e.g., Azure AI Search), and LLM response generation

• Integration of LLMs/agents with structured data sources (SQL/Snowflake), tools, and external systems/APIs

• Document processing and NLP skills: OCR, document parsing (e.g., Azure Document Intelligence/Form Recognizer), and extraction/classification/summarization/semantic search

• Azure AI development experience (Azure Machine Learning, Azure AI Studio, Azure OpenAI Service)

• Container and Azure deployment experience using Docker, Azure Container Apps, and ACR

• Production DevOps/MLOps experience with CI/CD using GitHub Actions or Azure DevOps

• Infrastructure as Code using Terraform or Bicep

• Monitoring and observability: Azure Monitor/Application Insights and alerts for drift/performance

• Senior expectations: architecture/roadmap ownership, code review/mentoring, documentation, and stakeholder communication

Benefits

Not specified in the provided posting.