Data Scientist

SKILLS AND COMPETENCIES

Technical Skills:

• Advanced proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow) • Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques • Experience with big data processing using Spark for large-scale data analytics • Version control and experiment tracking using Git and MLflow • Software Engineering & Development: Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing. • DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations. • LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management. • MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining. • Cloud & Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems. • LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security. • General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP. • Experience in creating LLD for the provided architecture. • Experience working in microservices based architecture.

Domain Expertise:

• Strong mathematical foundation in statistics, probability, linear algebra, and optimization • Deep understanding of ML and LLM development lifecycle, including fine-tuning and evaluation • Expertise in feature engineering, embedding optimization, and dimensionality reduction • Advanced knowledge of A/B testing, experimental design, and statistical hypothesis testing • Experience with RAG systems, vector databases, and semantic search implementation • Proficiency in LLM optimization techniques including quantization and knowledge distillation • Understanding of MLOps practices for model deployment and monitoring Professional Competencies: • Strong analytical thinking with ability to solve complex ML challenges • Excellent communication skills for presenting technical findings to diverse audiences • Experience translating business requirements into data science solutions • Project management skills for coordinating ML experiments and deployments • Strong collaboration abilities for working with cross-functional teams • Dedication to staying current with latest ML research and best practices • Ability to mentor and share knowledge with team members

Primary Skill Set:

Microservices LLM, Agentic AI Framework, Predictive modelling