AI Engineer Skills Gap Assessment

What the AI Engineer assessment covers

You answer yes or no to short statements about what you have actually done. Each statement belongs to one of 8 skill areas, and each area is weighted by how often employers ask for it, so the score reflects what jobs need.

Skill areas

• Python and software engineering. Python in 8 of 8 ML postings and half of AI postings, with production services, APIs, version control and testing; Go, TypeScript and React appear less often.
• Machine learning fundamentals. Applied ML from feature engineering to training, in PyTorch, TensorFlow, scikit-learn or XGBoost; supervised and unsupervised learning, plus algorithms.
• LLM applications, prompts and RAG. LLM APIs and platforms (OpenAI, Anthropic, Bedrock, Vertex AI), prompt engineering, RAG with vector databases, embeddings, retrieval and reranking, and context engineering.
• Agents and orchestration. LangGraph, LangChain, LlamaIndex, Google ADK and MCP, agent loops, tool calling, multi-agent workflows, guardrails and workflow orchestration with Airflow.
• Evaluation and quality. Evaluation pipelines and golden datasets, LLM-as-a-judge, error analysis, A/B testing and metrics design.
• Production ML, MLOps and monitoring. Production ML services, MLOps, monitoring for drift and data quality, ML pipelines, feature stores, canary releases, AWS and Databricks, Kubernetes and observability stacks.
• Communication, design and collaboration. Stakeholder and cross-functional communication, technical design documents, design reviews, mentoring and project leadership.
• Security, privacy and responsible AI. Authentication (OAuth, SSO), security controls, regulated data (HIPAA, health data privacy), ethical AI and compliance, plus enterprise governance.