About the role
Preferred candidate profile
Build and deploy GenAI/LLM-powered applications.
Develop APIs using FastAPI, design microservices-based AI systems, implement RAG pipelines, integrate Vector Databases, and deploy scalable AI workloads using Kubernetes.
Work on production-grade AI services.
Role & responsibilities
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.
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