AI Skills

Enterprises require AI skills that deliver measurable outcomes, anchored in reliable data, robust models, and governed deployment. Each skill aligns to a defined pipeline stage across data engineering, model development, orchestration, and application layers, and organizations map roles and timelines to those stages to deliver secure, auditable, and scalable AI

 Skills you will learn

Data Engineering

What it is: building reliable pipelines for clean, unified, analysis-ready data.

Pipeline: ingestion, transformation, quality validation, and storage.

Roles: Enterprise Architect, Data Architect, Data Engineer, Analytics Engineer.

Time to learn: foundational 4 to 8 weeks. proficiency 3 to 6 months.

Prerequisites: SQL, Python, basic statistics, cloud services, ETL concepts.

Knowledge Graphs

What it is: linking entities and relationships into a searchable semantic network.

Pipeline: semantic modeling, enrichment, and enterprise knowledge indexing.

Roles: Knowledge Engineer, Ontology Specialist, Enterprise Architect, Data Architect.

Time to learn: foundational 6 to 10 weeks. proficiency 4 to 8 months.

Prerequisites: graph databases, RDF and SPARQL, ontology design, data modeling.

Vector Databases

What it is: storing embeddings for fast semantic search and retrieval.

Pipeline: embedding generation, indexing, similarity search, and retrieval.

Roles: ML Engineer, Search Engineer, AI Platform Engineer, Enterprise Architect.

Time to learn: foundational 3 to 6 weeks. proficiency 2 to 4 months.

Prerequisites: embeddings, Python APIs, indexing strategies, similarity metrics.

Large Language Models

What it is: models that understand and generate human language at scale.

Pipeline: fine tuning, retrieval integration, inference, and monitoring.

Roles: AI Architect, ML Engineer, NLP Engineer, MLOps Engineer.

Time to learn: foundational 6 to 10 weeks. proficiency 6 to 12 months.

Prerequisites: Python, deep learning, transformers, GPU workflows.

Small Language Models

What it is: efficient language models for constrained compute and privacy.

Pipeline: distillation, quantization, edge deployment, and monitoring.

Roles: Edge AI Engineer, ML Engineer, Platform Engineer.

Time to learn: foundational 4 to 8 weeks. proficiency 4 to 6 months.

Prerequisites: model optimization, compression techniques, deployment tooling.

Natural Language Processing

What it is: techniques to interpret, classify, and generate language.

Pipeline: text preprocessing, modeling, inference, and evaluation.

Roles: NLP Engineer, Data Scientist, Product Analyst, AI Architect.

Time to learn: foundational 6 to 10 weeks. proficiency 4 to 8 months.

Prerequisites: Python, machine learning basics, tokenization, vectorization.

Named Entity Recognition

What it is: extracting people, organizations, amounts, and dates from text.

Pipeline: annotation, model training, inference, and postprocessing.

Roles: NLP Engineer, Data Analyst, Information Architect.

Time to learn: foundational 2 to 4 weeks. proficiency 1 to 3 months.

Prerequisites: spaCy or transformers, labeling tools, regex basics.

Embeddings

What it is: numeric vectors representing meaning for text, images, or audio.

Pipeline: embedding generation, normalization, storage, and reuse.

Roles: Data Scientist, ML Engineer, Search Engineer, Recommender Engineer.

Time to learn: foundational 2 to 4 weeks. proficiency 1 to 3 months.

Prerequisites: linear algebra, tokenization, embedding models, vector stores.

Reinforcement Learning

What it is: training agents to make sequential decisions via rewards.

Pipeline: environment design, policy training, evaluation, and deployment.

Roles: AI Researcher, Operations Research Analyst, Robotics Engineer.

Time to learn: foundational 8 to 12 weeks. proficiency 6 to 9 months.

Prerequisites: probability, calculus, Python, deep learning.

AI Agents

What it is: autonomous components that plan, act, and use tools.

Pipeline: planning, tool use, execution, memory, and evaluation.

Roles: AI Solution Architect, ML Engineer, Automation Engineer.

Time to learn: foundational 4 to 8 weeks. proficiency 3 to 6 months.

Prerequisites: API integration, prompts, function calling, safety guardrails.

Multi Agent Systems

What it is: coordinated agents collaborating or competing to solve tasks.

Pipeline: role assignment, communication, negotiation, and consensus.

Roles: Systems Architect, Simulation Engineer, AI Platform Engineer.

Time to learn: foundational 6 to 10 weeks. proficiency 6 to 9 months.

Prerequisites: distributed systems, messaging patterns, agent protocols.

Agent Orchestration

What it is: governing agent workflows for reliability, traceability, and compliance.

Pipeline: workflow definition, execution, observability, and feedback loops.

Roles: AI Platform Engineer, Enterprise Architect, Automation Engineer.

Time to learn: foundational 4 to 8 weeks. proficiency 3 to 6 months.

Prerequisites: orchestration frameworks, queues, tracing, circuit breakers.

Natural Language to SQL

What it is: translating questions into database queries automatically.

Pipeline: semantic parsing, schema grounding, query generation, validation.

Roles: BI Developer, Data Analyst, ML Engineer, Data Architect.

Time to learn: foundational 2 to 4 weeks. proficiency 1 to 3 months.

Prerequisites: SQL, database schemas, intent classification, constraints.

Natural Language to Python

What it is: generating safe Python code from natural language instructions.

Pipeline: intent parsing, code generation, execution, and verification.

Roles: Software Engineer, Data Scientist, Automation Engineer.

Time to learn: foundational 2 to 4 weeks. proficiency 1 to 3 months.

Prerequisites: Python fundamentals, testing, sandboxing, linting.

Retrieval Augmented Generation

What it is: grounding model outputs with trusted enterprise knowledge.

Pipeline: chunking, embeddings, retrieval, prompting, and response ranking.

Roles: AI Engineer, Knowledge Manager, MLOps Engineer, Enterprise Architect.

Time to learn: foundational 4 to 8 weeks. proficiency 3 to 6 months.

Prerequisites: embeddings, vector databases, prompt templates, evaluation.

Prompt Engineering

What it is: structuring instructions that consistently guide model behavior.

Pipeline: prompt design, templating, testing, and guardrail tuning.

Roles: Prompt Engineer, UX Writer, Product Manager, NLP Engineer.

Time to learn: foundational 1 to 3 weeks. proficiency 1 to 2 months.

Prerequisites: clear writing, task decomposition, model constraints, evaluation.

Data Engineering

What it is: building reliable pipelines for clean, unified, analysis-ready data.

Pipeline: ingestion, transformation, quality validation, and storage.

Roles: Enterprise Architect, Data Architect, Data Engineer, Analytics Engineer.

Time to learn: foundational 4 to 8 weeks. proficiency 3 to 6 months.

Prerequisites: SQL, Python, basic statistics, cloud services, ETL concepts.

Knowledge Graphs

What it is: linking entities and relationships into a searchable semantic network.

Pipeline: semantic modeling, enrichment, and enterprise knowledge indexing.

Roles: Knowledge Engineer, Ontology Specialist, Enterprise Architect, Data Architect.

Time to learn: foundational 6 to 10 weeks. proficiency 4 to 8 months.

Prerequisites: graph databases, RDF and SPARQL, ontology design, data modeling.

Vector Databases

What it is: storing embeddings for fast semantic search and retrieval.

Pipeline: embedding generation, indexing, similarity search, and retrieval.

Roles: ML Engineer, Search Engineer, AI Platform Engineer, Enterprise Architect.

Time to learn: foundational 3 to 6 weeks. proficiency 2 to 4 months.

Prerequisites: embeddings, Python APIs, indexing strategies, similarity metrics.

Large Language Models

What it is: models that understand and generate human language at scale.

Pipeline: fine tuning, retrieval integration, inference, and monitoring.

Roles: AI Architect, ML Engineer, NLP Engineer, MLOps Engineer.

Time to learn: foundational 6 to 10 weeks. proficiency 6 to 12 months.

Prerequisites: Python, deep learning, transformers, GPU workflows.

Small Language Models

What it is: efficient language models for constrained compute and privacy.

Pipeline: distillation, quantization, edge deployment, and monitoring.

Roles: Edge AI Engineer, ML Engineer, Platform Engineer.

Time to learn: foundational 4 to 8 weeks. proficiency 4 to 6 months.

Prerequisites: model optimization, compression techniques, deployment tooling.

Natural Language Processing

What it is: techniques to interpret, classify, and generate language.

Pipeline: text preprocessing, modeling, inference, and evaluation.

Roles: NLP Engineer, Data Scientist, Product Analyst, AI Architect.

Time to learn: foundational 6 to 10 weeks. proficiency 4 to 8 months.

Prerequisites: Python, machine learning basics, tokenization, vectorization.

Named Entity Recognition

What it is: extracting people, organizations, amounts, and dates from text.

Pipeline: annotation, model training, inference, and postprocessing.

Roles: NLP Engineer, Data Analyst, Information Architect.

Time to learn: foundational 2 to 4 weeks. proficiency 1 to 3 months.

Prerequisites: spaCy or transformers, labeling tools, regex basics.

Embeddings

What it is: numeric vectors representing meaning for text, images, or audio.

Pipeline: embedding generation, normalization, storage, and reuse.

Roles: Data Scientist, ML Engineer, Search Engineer, Recommender Engineer.

Time to learn: foundational 2 to 4 weeks. proficiency 1 to 3 months.

Prerequisites: linear algebra, tokenization, embedding models, vector stores.

Reinforcement Learning

What it is: training agents to make sequential decisions via rewards.

Pipeline: environment design, policy training, evaluation, and deployment.

Roles: AI Researcher, Operations Research Analyst, Robotics Engineer.

Time to learn: foundational 8 to 12 weeks. proficiency 6 to 9 months.

Prerequisites: probability, calculus, Python, deep learning.

AI Agents

What it is: autonomous components that plan, act, and use tools.

Pipeline: planning, tool use, execution, memory, and evaluation.

Roles: AI Solution Architect, ML Engineer, Automation Engineer.

Time to learn: foundational 4 to 8 weeks. proficiency 3 to 6 months.

Prerequisites: API integration, prompts, function calling, safety guardrails.

Multi Agent Systems

What it is: coordinated agents collaborating or competing to solve tasks.

Pipeline: role assignment, communication, negotiation, and consensus.

Roles: Systems Architect, Simulation Engineer, AI Platform Engineer.

Time to learn: foundational 6 to 10 weeks. proficiency 6 to 9 months.

Prerequisites: distributed systems, messaging patterns, agent protocols.

Agent Orchestration

What it is: governing agent workflows for reliability, traceability, and compliance.

Pipeline: workflow definition, execution, observability, and feedback loops.

Roles: AI Platform Engineer, Enterprise Architect, Automation Engineer.

Time to learn: foundational 4 to 8 weeks. proficiency 3 to 6 months.

Prerequisites: orchestration frameworks, queues, tracing, circuit breakers.

Natural Language to SQL

What it is: translating questions into database queries automatically.

Pipeline: semantic parsing, schema grounding, query generation, validation.

Roles: BI Developer, Data Analyst, ML Engineer, Data Architect.

Time to learn: foundational 2 to 4 weeks. proficiency 1 to 3 months.

Prerequisites: SQL, database schemas, intent classification, constraints.

Natural Language to Python

What it is: generating safe Python code from natural language instructions.

Pipeline: intent parsing, code generation, execution, and verification.

Roles: Software Engineer, Data Scientist, Automation Engineer.

Time to learn: foundational 2 to 4 weeks. proficiency 1 to 3 months.

Prerequisites: Python fundamentals, testing, sandboxing, linting.

Retrieval Augmented Generation

What it is: grounding model outputs with trusted enterprise knowledge.

Pipeline: chunking, embeddings, retrieval, prompting, and response ranking.

Roles: AI Engineer, Knowledge Manager, MLOps Engineer, Enterprise Architect.

Time to learn: foundational 4 to 8 weeks. proficiency 3 to 6 months.

Prerequisites: embeddings, vector databases, prompt templates, evaluation.

Prompt Engineering

What it is: structuring instructions that consistently guide model behavior.

Pipeline: prompt design, templating, testing, and guardrail tuning.

Roles: Prompt Engineer, UX Writer, Product Manager, NLP Engineer.

Time to learn: foundational 1 to 3 weeks. proficiency 1 to 2 months.

Prerequisites: clear writing, task decomposition, model constraints, evaluation.

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