Practices
AI and data expertise across the delivery lifecycle
AI application engineering
Python engineers building RAG, agents, model integrations, evaluations, and secure application experiences.
ML & deep learning
PyTorch and TensorFlow practitioners for training, adaptation, inference, optimization, and model validation.
Data platform engineering
Databricks and Snowflake architects, data engineers, analytics engineers, and governance specialists.
MLOps & LLMOps
Reproducible pipelines, model registries, deployment, monitoring, quality evaluation, drift, and cost management.
Skill matrix
Frameworks, platforms, and controls
Frameworks
- Python
- PyTorch
- TensorFlow
- Hugging Face
- LangChain
- LlamaIndex
Data & retrieval
- Pinecone
- Weaviate
- Milvus
- Snowflake
- Databricks
- Apache Spark
Operations
- MLflow
- MLOps
- LLMOps
- Model monitoring
- Evaluation harnesses
- Feature pipelines
Enterprise controls
- AI governance
- PII protection
- Human approval
- Model risk
- Audit logging
- Responsible AI