Skill Assessments

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Large language models, prompt engineering, RAG systems, fine-tuning, and generative AI application development.

Core machine learning and deep learning competencies spanning supervised, unsupervised, and reinforcement learning.

Skills for taking ML systems from experimentation to reliable production.

Text processing, embeddings, sequence labeling, and advanced NLP pipeline construction.

Theoretical foundations of AI, paper replication, novel algorithm implementation, and experimental design.

Skills for building systems that reason, plan, and act autonomously.

Niche but high-growth skill areas including edge AI, speech/audio, multimodal systems, and AI ethics.

Embed directly with customers to deliver AI platforms in production environments. Covers Python, data engineering, cloud infrastructure, API integrations, LLM/RAG, MLOps, distributed systems, and security governance.

Design, build, and operate platforms and pipelines that make LLM features reliable, secure, cost-effective, and measurable in production.

Design the cognitive architecture behind autonomous agent systems — planning loops, memory management, tool integration, multi-agent coordination, and governance guardrails.

Reduce real-world risk in deployed AI systems by inventing, validating, and operationalizing AI safety evaluations, mitigations, and governance-ready evidence.