▷ (Urgent Search) Data Scientist

Job Description

Responsibilities:

- Design, build, and deploy Generative AI models using foundational models like GPT, BERT, LLaMA, PaLM, etc.
- Develop scalable GenAI applications and integrate with enterprise systems using APIs and SDKs.
- Fine-tune and optimize large language models (LLMs) for domain-specific use cases.
- Design, implement, and manage end-to-end machine learning pipelines on Microsoft Azure, leveraging services like Azure Machine Learning, Azure DevOps, and Kubernetes.
- Collaborate with data scientists to productionize ML models using best practices in Azure MLOps.
- Automate the deployment and monitoring of models using CI/CD pipelines and Azure DevOps tools.
- Implement scalable model training, validation, and deployment workflows in the cloud.
- Monitor model performance in production and retrain models as needed to maintain accuracy and reliability.
- Ensure security, compliance, and governance of ML workflows and data.
- Develop Python scripts and tools to automate repetitive tasks and improve operational efficiency.
- Troubleshoot and optimize ML workflows for performance and cost-effectiveness.
- Document architecture, processes, and operational procedures.

Qualification:

- Hands-on experience with transformer-based models (e.g., GPT, BERT, LLaMA, etc.)
- Familiarity with tools like LangChain, LlamaIndex, Haystack, etc.
- Experience in prompt engineering, retrieval-augmented generation (RAG), and model fine-tuning.
- Proven experience in MLOps, specifically with Azure services.
- Strong programming skills in Python
- Experience with Hugging Face Transformers, PyTorch or TensorFlow.
- REST APIs and/or gRPC for model integration.
- Experience with Azure Databricks, Azure Machine Learning, Azure OpenAI
- Familiarity with ML libraries (scikit-learn, TensorFlow, PyTorch).
- Experience building and managing CI/CD pipelines for ML models using Azure DevOps or equivalent tools.
- Building REST APIs for ML inference using frameworks like FastAPI or Flask.
- Understanding of containerization technologies like Docker and orchestration using Kubernetes.
- Knowledge of machine learning lifecycle management, model versioning, and deployment strategies.
- Experience with data engineering, data pipelines, and ETL processes on Azure.
- Familiarity with monitoring tools and logging frameworks for production systems.
- Strong problem-solving skills and ability to work in a collaborative, fast-paced environment.

Technical Skills:

- GenAI models: GPT, BERT, LLaMA, PaLM
- Framework: PyTorch or TensorFlow
- Cloud Platforms: Microsoft Azure (Azure ML, Azure Databricks, AKS, Azure DevOps)
- Programming Languages: Python
- CI/CD Tools: Azure DevOps, GitHub Actions, Jenkins
- Containerization: Docker, Kubernetes
- Data Storage & Processing: Azure Blob Storage, Azure SQL, Azure Data Factory
- Monitoring & Logging: Azure Monitor, Application Insights, Prometheus, Grafana

Mandatory skills

GPT/BERT/LLaMA, MLOps, Python, Azure Databricks, Azure Machine Learning, Azure OpenAI, GenAI applications, Transformer-based models

Works in the area of Software Engineering, which encompasses the development, maintenance and optimization of software solutions/applications.1. Applies scientific methods to analyse and solve software engineering problems.2. He/she is responsible for the development and application of software engineering practice and knowledge, in research, design, development and maintenance.3. His/her work requires the exercise of original thought and judgement and the ability to supervise the technical and administrative work of other software engineers.4. The software engineer builds skills and expertise of his/her software engineering discipline to reach standard software engineer skills expectations for the applicable role, as defined in Professional Communities.5. The software engineer collaborates and acts as team player with other software engineers and stakeholders.

Job Description - Grade Specific

Has more than a year of relevant work experience. Solid understanding of programming concepts, software design and software development principles. Consistently works to direction with minimal supervision, producing accurate and reliable results. Individuals are expected to be able to work on a range of tasks and problems, demonstrating their ability to apply their skills and knowledge. Organises own time to deliver against tasks set by others with a mid term horizon. Works co-operatively with others to achieve team goals and has a direct and positive impact on project performance and make decisions based on their understanding of the situation, not just the rules.