Direct client need for Sr AI Engineer C2C jobs Urgent Need

Job Title: Sr AI Engineer

Location: Remote and travel to MI

Duration: Longterm

Note: Required AI Engineer with a Data Scientist background

Technologies: Sql, Azure Stack, Python LLM prompt engineering

Implementing OCR as a service is an Azure-native, real time event-driven scalable document OCR pipeline that processes multi-page documents in parallel using Azure stack solutions and docTR.

Building an AI agent that processes insurance claim PDFs through multi-modal extraction (text, layout, images), generates comprehensive summaries, integrates with line-of-business and transactional databases to surface discrepancies, detect claim bias, and evaluate demand packages — with interactive querying for adjusters throughout. Sql, Azure Stack, Python LLM prompt engineering

The Data Engineer will support the Data Science team by building and maintaining Azure-based data and document-processing pipelines. This role spans ingestion through extraction and model-ready transformation, enabling advanced analytics, classification, and decision-intelligence workflows.

Core Responsibilities:

• Azure Pipeline Development:
– Build scalable ingestion, preprocessing, classification, and extraction pipelines using Azure Data Factory, Azure Databricks, ADLS, and Azure Functions.

• Document Processing:
– Implement OCR, NLP, and LLM-based extraction workflows using Cognitive Services or custom Databricks models.

• Orchestration & Runtime Management:
– Manage workflow orchestration, monitoring, and error handling with ADF, Databricks Workflows, and Azure Monitor.

• Data Science Enablement:
– Deliver clean, structured, model-ready datasets using Delta Lake and Databricks.
– Support Azure ML deployments and integrations with downstream Q&A and retrieval systems.

• Integration & Decision Intelligence:
– Integrate processed outputs with decision-intelligence layers, search systems, and analytics dashboards (e.g., Power BI, Azure Search).

• Scalability, Governance & Best Practices:
– Ensure pipelines meet Azure security, governance, and performance standards.
– Implement CI/CD, versioning, and automation for reliable, production-grade workflows.