AI Engineer or Data scientist (Azure AI and Generative AI)

Job Title:

About the Role

We’re looking for an AI Engineer / Data Scientist to design, build, and deploy AI solutions on the Microsoft Azure ecosystem. You’ll work on Generative AI, machine learning, deep learning, and advanced analytics to deliver production-ready AI capabilities for real business use cases.

Key Responsibilities

• Build and deploy ML/DL models on

  Azure Machine Learning

  with full lifecycle ownership.
• Develop

  Generative AI applications

  using

  Azure OpenAI

  (prompting, RAG, agents, evaluation).
• Create scalable data/ML pipelines using

  Azure Databricks/Synapse

  .
• Implement intelligent search and retrieval using

  Azure AI Search (vector search included)

  .
• Integrate AI services with event-driven systems using

  Event Hub / Service Bus

  .
• Fine-tune and optimize LLMs (LoRA/PEFT), and apply model compression/optimization techniques.
• Develop APIs/microservices to serve models in production (FastAPI/Flask, Docker, AKS).
• Set up monitoring and governance for ML/GenAI solutions (MLOps/LLMOps).
• Work with product and engineering teams to translate requirements into deployable AI solutions.

Required Skills & Experience (4–6 years)

• 4–6 years hands-on experience in

  AI/ML/Data Science

  with strong Python skills.
• Strong foundation in

  ML, Deep Learning, NLP, and Statistics

  .
• Experience with

  Azure ML

  (training, pipelines, endpoints, model registry, monitoring).
• Experience building GenAI solutions with

  Azure OpenAI

  , including:
• Prompt engineering and safety patterns

• RAG architectures

  (embeddings, retrieval tuning, evaluation)
• Agents/tool/function calling
• Hands-on experience with Azure services:

• Azure AI Search

  (including vector search)

• Cosmos DB

• Azure Databricks and/or Synapse Analytics

• Event Hub / Service Bus

• Strong knowledge of vector databases:

• Pinecone, FAISS, Weaviate, or Azure AI Search vectors

• Experience with LLM fine-tuning & optimization:

• LoRA, PEFT/adapters

• Quantization/distillation or other optimization methods
• Applied experience in at least one area:

• Time-series forecasting

• Recommender systems

• NLP or multimodal modeling

• Production engineering skills: REST APIs, Docker, CI/CD, and cloud deployment.

  Role & responsibilities

Preferred candidate profile