Must-have

• Advanced proficiency in Python
• LLM frameworks (Hugging Face Transformers, LangChain)
• Big data processing using Spark
• Version control and experiment tracking using Git and MLflow
• Microservices, test-driven development, concurrency processing
• Infrastructure as Code (Terraform, CloudFormation)
• CI/CD pipelines (GitHub Actions, Jenkins)
• Container orchestration (Kubernetes) with Helm
• LLM serving platforms (vLLM, FastAPI)
• Model quantization techniques
• Vector database management
• Containerization for ML workloads
• Model serving tools (TorchServe, TF Serving)
• Automated model retraining
• Advanced cloud services (AWS, GCP, Azure)
• Network security for ML systems
• Developing chatbots, recommendation systems, translation services
• Optimizing LLMs for performance and security
• SQL, machine learning frameworks
• Creating LLD for provided architecture
• Microservices based architecture
• ML and LLM development lifecycle
• Fine-tuning and evaluation
• Feature engineering, embedding optimization, dimensionality reduction
• A/B testing, experimental design, statistical hypothesis testing
• RAG systems, vector databases, semantic search implementation
• LLM optimization techniques
• MLOps practices for model deployment and monitoring
• GenAI / LLM Engineers (Python, FastAPI, Microservices, K8s, RAG, VectorDBs)

Nice-to-have

• Prior experience in industries leveraging AI/ML
• AI/ML Machine Learning
• GEN AI Agentic AI
• Embracing Change
• Intellectual Curiosity
• Learning Agility
• Inclusion