Position Summary
We are seeking an experienced
AI/ML Engineer
with hands-on expertise in
Azure Document Intelligence
and
LLM-based solutions
. The ideal candidate will design, develop, and deploy intelligent document processing and natural language processing (NLP) applications, leveraging Microsoft Azure’s scalable ecosystem. This role requires strong technical depth in large language model (LLM) integration, cloud-based architecture, and modern AI-driven enrichment pipelines.
Key Responsibilities
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Document Processing Solutions
Design and implement
document processing pipelines
using
Azure Document Intelligence
for data extraction, classification, and enrichment.
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LLM Integration
Develop and fine-tune
LLM-based applications
for tasks such as text summarization, document understanding, and data enrichment.
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Azure Cloud Development
Leverage
Azure services
(Functions, Cognitive Services, AI Studio, Storage, Key Vault) for scalable, serverless, and secure AI deployments.
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AI Solution Design
Build
end-to-end AI workflows
combining traditional machine learning models, large language models, and API-driven automation.
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Performance Optimization
Evaluate performance, optimize model efficiency, and ensure high availability across production systems.
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Collaboration
Work closely with cross-functional teams including data engineers, architects, and business stakeholders to align solutions with enterprise goals.
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Documentation And Best Practices
Document architecture, design patterns, and reproducible workflows for future reference and compliance standards.
Mandatory Requirements
Skills & Experience Required
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> 5–7 years of experience in AI/ML or Data Engineering roles.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Proven experience developing document processing solutions using Azure Document Intelligence (Form Recognizer or Cognitive Search pipelines).
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Hands-on experience in building LLM-based applications, demonstrating strong knowledge of Natural Language Processing (NLP) and AI enrichment techniques.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Proficiency in Microsoft Azure services, particularly Azure Functions, Azure Cognitive Services, and Serverless architecture for scalable model deployment.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Experience with Python, REST APIs, and popular machine learning frameworks (PyTorch, TensorFlow, Scikit-learn).
Good To Have
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Strong foundation in traditional machine learning, including end-to-end model lifecycle management (data prep, model training, validation, and deployment).
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Familiarity with Azure OpenAI, Prompt Engineering, or APIs for LLMs (GPT, Gemini, Claude, etc.).
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Experience with MLOps or CI/CD pipelines for machine learning deployment.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Working knowledge of containerization tools (Docker, Kubernetes) for AI workloads.
Qualifications
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Relevant certifications in Azure AI or Data Engineering (e.g., Microsoft Certified: Azure AI Engineer Associate) are an advantage.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Strong communication and collaboration skills with the ability to explain technical concepts to non-technical stakeholders.
Skills: ml,artificial intelligence,open ai,ai/ml,llm,azure,machine learning,ai,microsoft azure,mlops
