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Job Description & How to Apply Below
- Build and maintain scalable AI/ML pipelines for pretraining, fine-tuning, inference, and continuous learning, integrating advanced data preprocessing and retrieval-augmented generation (RAG) techniques.
- Develop APIs and embeddings-based integrations to seamlessly connect LLMs and GenAI models with enterprise applications, knowledge bases, and workflows.
- Implement prompt engineering and model orchestration strategies to enhance performance, control AI behaviour, and mitigate hallucinations in production environments.
- Monitor, evaluate, and troubleshoot AI models for performance, fairness, and compliance, leveraging automated feedback loops, human-in-the-loop (HITL) frameworks, and explainability techniques.
- Conduct research on the latest advancements in GenAI and LLM architectures (Mixture of Experts, Transformer optimizations, multimodal models, etc.) to drive innovation and refine AI strategy.
- Collaborate with data engineering and MLOps teams to ensure robust data pipelines, vector databases, and scalable cloud infrastructure for AI deployment.
- Leverage Azure AI services and cloud platforms (Azure OpenAI, Azure Machine Learning, Databricks, and Vector Search) for model training, deployment, and real-time inference.
- Implement best practices for AI governance, reproducibility, and CI/CD automation to streamline the model lifecycle while ensuring security, compliance, and ethical AI.
- Strong understanding of transformer architectures (GPT, BERT, T5) and LLM fine-tuning.
- Experience with prompt engineering, RAG (Retrieval-Augmented Generation), and embeddings.
- Proficiency in NLP, deep learning, and generative AI techniques.
- Knows how to build agentic architectures & AI-agents.
- Hands‑on experience with Azure OpenAI Service, Azure AI Search, and Azure Cognitive Services.
- Familiarity with Azure Machine Learning (Azure ML) for model training, fine‑tuning, and deployment.
- Proficiency in Python, PyTorch, Tensor Flow, Hugging Face, Lang Chain, and Llama Index.
- Experience deploying AI models using Azure Functions, API Management, and Kubernetes (AKS).
- Strong grasp of Azure SDK, Dev Ops, and CI/CD for AI pipelines.
- Knowledge of Azure Blob Storage, Cosmos DB, and vector databases for LLM‑powered applications.
- Experience with scalable AI architectures and GPU optimization.
- Understanding of data privacy, model security (prompt injection, hallucination mitigation), and Microsoft Purview.
- Experience in deploying enterprise‑grade AI applications with ethical AI best practices.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
- Over 10 years of experience in the field, with a minimum of 5 years of experience as an AI Developer, with a strong focus on building and deploying machine learning models.
- Experience in the implementation of solutions using generative AI models (OpenAI services including GPT and embedding models and other popular open‑source models).
- Proficiency in programming languages such as Python, R, or Java, and experience with ML frameworks like Tensor Flow, Keras, or PyTorch.
- Demonstrated experience in implementing ML models in real‑world scenarios, with case studies or portfolio examples.
- Experience in improving model performance including optimization techniques, hyperparameter tuning, performance monitoring.
- Strong understanding of data structures, data modelling, and software architecture.
- Experience with cloud platforms, particularly Microsoft Azure, is highly desirable.
- Experience with low‑code/no‑code platforms is highly desirable.
- Good understanding of data management principles, data warehousing, and business intelligence concepts.
- Excellent analytical, problem‑solving, and communication skills.
- Ability to work collaboratively in a team environment.
- Ability to work in a fast‑paced and dynamic environment.
- Ability to learn new technologies and tools quickly.
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