Senior AI Solutions Engineer

Required Qualifications

Core Programming & Tooling

• Strong programming proficiency in Python, producing production-quality, well-structured code.
• Proficient in SQL; experience with large-volume datasets in Spark and/or Snowflake.
• Comfortable scripting in Python and Bash for automation and tooling.
• Expertise with Git for version control, branching strategies, code review, and pull request workflows.
• Familiarity with experiment tracking and model versioning (e.g., MLflow).

Snowflake

• Data lake vs data warehouse architecture (medallion architecture: bronze/silver/gold)
• API integration patterns for ingesting from source systems
• Schema evolution and handling semi-structured data
• Advanced SQL (window functions, CTEs, recursive queries, semi-structured data with VARIANT/JSON)
• Warehouse sizing, scaling, and cost optimization
• Data sharing, Time Travel, Zero-Copy Cloning
• Snowpipe for streaming/continuous ingestion
• Role-based access control (RBAC), security, and data governance
• Query performance tuning (clustering keys, micro-partitions, query profiling)
• Streams and Tasks for CDC and orchestration
• Snowpark (Python/Java integration) is increasingly valuable
• Model development (staging/intermediate/mart layers)
• Jinja templating, macros, and incremental models
• Snapshots for SCD Type 2
• dbt testing (schema tests, dbt-utils)
• Version control: Git/GitHub or Azure Repos, branching strategies
• Orchestration: Airflow, Azure Data Factory, or Dagster
• Data modeling: Dimensional modeling (Kimball), star/snowflake schemas
• CI/CD: Automated testing and deployment for dbt models and pipelines

Machine Learning & Statistical Modeling

• Hands-on experience building and deploying ML on large-scale production datasets.
• Regression, classification, clustering, and ensemble methods.
• Time series forecasting.
• Optimization modeling (linear programming, mixed-integer programming).
• Feature engineering, data wrangling, backtesting, and statistical analysis on structured data.
• Sound evaluation practice: proper train/validation/test splits, leakage avoidance, and forward/out-of-sample validation.

LLM Applications & AI Agents

• AI agent development: building agentic frameworks using Azure AI Foundry and Azure model endpoints, including tool-use patterns and multi-step orchestration.
• Experience with orchestration frameworks such as LangChain or LlamaIndex.
• Prompt engineering and chain-of-thought design for complex, multi-step workflows.
• RAG pipeline development: chunking, vector embedding/indexing (Azure AI Search), and response generation using LLM endpoints.
• Integrating LLMs and agents with structured data sources (SQL/Snowflake), tools, and external systems/APIs.
• Grounding, citation, and evaluation techniques to control hallucination and verify model outputs.

Document Processing, OCR & NLP

• OCR and document parsing using Azure Document Intelligence / Form Recognizer (or equivalent).
• Extracting and structuring information from unstructured and semi-structured documents at scale (PDFs, scans, forms, tables).
• Text processing and NLP fundamentals: entity extraction, classification, summarization, and semantic search.
• LLM-based extraction, comparison, and term/clause analysis across long documents.
• Handling document layout, multi-page context, and noisy/low-quality inputs.

Azure, Containers & Compute

• Core Azure resources: Azure Machine Learning, Azure AI Studio, Azure OpenAI Service, and Azure storage/compute.
• Docker: building, optimizing, and managing container images for ML and AI applications.
• Azure Container Apps (ACA): containerized deployment, KEDA-based autoscaling, and replica management.
• Azure Container Registry (ACR): image management, automated builds, and vulnerability scanning.
• Familiarity with serverless compute such as Azure Functions for targeted tasks.

MLOps, CI/CD & Deployment

• Hands-on DevOps/MLOps experience in production environments.
• CI/CD pipeline design and maintenance for ML and LLM workloads using GitHub Actions or Azure DevOps.
• Infrastructure as Code with Terraform or Bicep for reproducible Azure environments.
• Managing environment promotion and configuration across dev, staging, and prod.
• Azure Machine Learning model lifecycle: model registry, automated retraining pipelines, model versioning, and endpoint management.
• Deployment strategies (blue-green, canary), rollback procedures, and automated testing for ML pipelines (integration, load, security).
• Deploying and scaling LLM-based applications and agent infrastructure on Azure, with attention to latency, throughput, and cost.

Monitoring, Observability & Delivery

• Delivering production-ready models and services with proper testing, validation, and monitoring.
• Azure Monitor and Application Insights: logging, tracing, custom metrics, and alerting.
• Model performance monitoring: data drift detection, prediction quality tracking, and automated alerts.
• Basic UI skills (Streamlit or Gradio) for quick prototyping and stakeholder demos.

Soft/Leadership Skills (Senior-level expectations )

• Technical roadmap ownership / architecture decisions
• Setting engineering standards (code review bar, testing standards) across both DE and DS work
• Ability to review code/PRs and mentor junior engineers
• Stakeholder communication (translating technical concepts to business users)
• Documentation and knowledge-sharing practices
• Cost optimization mindset (especially critical in Snowflake/Azure environments)
• Cross-functional collaboration with analytics, BI, and data science teams
• Incident management and troubleshooting production data issues