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

Must Have : Microservices, LLM,  Python, FastAPI, Vector DB(Qdrant, Chromadb, stc), RAG, MLOps & Deployment, Cloud, Agentic AI Framework, Kubernetes