Senior ML Engineer (GenAI, AWS)

Machine Learning Core

• ML Fundamentals: supervised, unsupervised, and reinforcement learning;
• Model Development: feature engineering, model training, evaluation, hyperparameter tuning, and validation;
• ML Frameworks: classical ML libraries, TensorFlow, PyTorch, or similar frameworks;
• Deep Learning: CNNs, RNNs, Transformers.

LLMs and Generative AI

• LLM Applications: Experience building production LLM-based applications;
• Prompt Engineering: Ability to design effective prompts and chain-of-thought strategies;
• RAG Systems: Experience building retrieval-augmented generation architectures;
• Vector Databases: Familiarity with embedding models and vector search;
• LLM Evaluation: Experience with evaluation metrics and techniques for LLM outputs.

Data and Programming

• Python: Advanced proficiency in Python for ML applications;
• Data Manipulation: Expert with pandas, numpy, and data processing libraries;
• SQL: Ability to work with structured data and databases;
• Data Pipelines: Experience building ETL/ELT pipelines - Big Data: Experience with Spark or similar distributed computing frameworks.

MLOps and Production

• Model Deployment: Experience deploying ML models to production environments;
• Containerization: Proficiency with Docker and container orchestration;
• CI/CD: Understanding of continuous integration and deployment for ML;
• Monitoring: Experience with model monitoring and observability;
• Experiment Tracking: Familiarity with MLflow, Weights and Biases, or similar tools.

Cloud and Infrastructure

• AWS Services: Strong experience with AWS ML services (SageMaker, Lambda, etc.); -GCP Expertise: Advanced knowledge of GCP ML and data services;

• Cloud Architecture: Understanding of cloud-native ML architectures;

• Infrastructure as Code: Experience with Terraform, CloudFormation, or similar.