Job
Description
Technical Skills: Advanced proficiency in Python . 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, Architecture & Design Secondary Skills - Data science, ML and NLP Required Skills Artificial Intelligence,Machine Learning,Data Analysis
