hr-ai

HR AI

Comprehensive AI and Machine Learning knowledge for HR and recruiters — from understanding modern AI ecosystems and LLM workflows to evaluating AI candidates, interpreting portfolios, and improving technical hiring decisions.

Supported tasks

• Explaining AI and machine learning concepts for non-technical recruiters
• Understanding modern AI ecosystems and LLM workflows
• Screening AI Engineers, ML Engineers, and Applied AI candidates effectively
• Evaluating AI portfolios, demos, GitHub repositories, and research projects
• Creating AI interview questions and hiring scorecards
• Comparing AI Engineering, Machine Learning, Data Science, and LLM Engineering roles
• Understanding AI infrastructure and production AI workflows
• Identifying AI seniority levels and skill expectations
• Understanding generative AI, autonomous agents, and multimodal systems
• Writing AI-related job descriptions and hiring requirements
• Explaining AI terminology used by engineers and researchers
• Understanding collaboration between AI, data, backend, product, and infrastructure teams

What AI engineering means in 2026

Modern AI engineering is no longer:

• "just training machine learning models"
• "only building chatbots"
• "just prompt engineering"

In 2026, modern AI systems increasingly include:

• LLM applications
• agentic AI systems
• multimodal AI
• retrieval-augmented generation (RAG)
• AI infrastructure
• vector databases
• AI observability
• autonomous workflows
• AI orchestration
• AI product integration

Modern AI teams are increasingly expected to support:

• product automation
• intelligent workflows
• AI copilots
• enterprise AI systems
• recommendation systems
• AI-driven analytics
• AI-assisted software development

Agentic AI and multi-agent systems are becoming major industry trends in 2026.

AI ecosystem (2026)

Core AI and ML frameworks

• PyTorch
• TensorFlow
• Scikit-learn
• JAX

Generative AI and LLM ecosystems

• OpenAI APIs
• Anthropic APIs
• Hugging Face
• LangChain
• LlamaIndex

Vector databases and retrieval systems

• Pinecone
• Weaviate
• ChromaDB
• Qdrant

AI infrastructure and orchestration

• Kubernetes
• Ray
• MLflow
• Kubeflow
• BentoML

Data and AI processing

• Python
• Pandas
• Polars
• Apache Spark

AI deployment and observability

• Weights & Biases
• Langfuse
• Arize AI
• Datadog

AI coding ecosystems

• Cursor
• GitHub Copilot
• Claude Code
• Replit
• Bolt.new

AI-assisted development workflows are rapidly changing software engineering and AI product development.

Types of AI-related roles

Machine Learning Engineer

Focuses on:

• ML systems
• model deployment
• production pipelines
• scalability
• inference systems

AI Engineer

Focuses on:

• LLM applications
• AI products
• orchestration systems
• retrieval systems
• AI integrations

Applied AI Engineer

Focuses on:

• integrating AI into products
• user-facing AI workflows
• AI automation
• product experimentation

Research Engineer

Focuses on:

• experimentation
• model optimization
• research implementation
• AI system evaluation

AI Infrastructure Engineer

Focuses on:

• model serving
• distributed systems
• GPU infrastructure
• AI scalability
• inference optimization

Prompt Engineer

Focuses on:

• prompt optimization
• AI workflow tuning
• LLM interaction patterns

However, pure "Prompt Engineer" roles are becoming less common as companies increasingly expect broader AI engineering capabilities.

Key prompts

AI fundamentals

1. "Explain AI engineering and its sub-fields in simple terms for [non-technical recruiters]."
2. "What does an [AI/ML Engineer] actually do day to day in [startup vs enterprise]?"
3. "Compare the roles of [AI Engineer, ML Engineer, Data Scientist, and Research Engineer] to help me plan hiring for [our new AI team]."
4. "Why are companies investing heavily in [generative AI and LLM integration]?"
5. "What AI skills are most important for [Applied AI Engineer vs ML Infrastructure Engineer] in 2026?"

Generative AI and LLMs

1. "Explain LLMs and their core architectures (for example, transformer models) for [technical recruiters screening candidates]."
2. "What is the difference between [generative AI] and [traditional predictive machine learning]?"
3. "What is RAG (retrieval-augmented generation) and why do companies use it in [enterprise search or customer support applications]?"
4. "What are [AI agents, multi-agent orchestration, and autonomous workflows]?"
5. "What AI ecosystem trends should recruiters understand when hiring in [2026]?"

AI infrastructure and production

1. "Explain the challenges of moving AI systems from [concept/prototype] to [production/scale]."
2. "Why are vector databases (for example, Pinecone, Weaviate) important in [Applied AI applications]?"
3. "What infrastructure and distributed systems skills (for example, Kubernetes, Ray) are expected from a [Senior/Staff AI Engineer]?"
4. "What AI orchestration workflows are common in [modern AI engineering teams]?"
5. "What model serving and observability tooling (for example, BentoML, Langfuse, Weights & Biases) should recruiters recognize on resumes for [MLOps/AI Platform roles]?"

AI candidate screening

1. "How can I evaluate the technical depth of an [AI Engineer] candidate without having a highly technical background?"
2. "What are major red flags when screening [Applied AI vs Research Engineer] candidates?"
3. "What should I look for when evaluating an AI candidate's [portfolio, GitHub repository, or research publication]?"
4. "How do I distinguish between [Junior, Middle, Senior, and Staff] AI engineers in terms of their systems thinking and architectural ownership?"
5. "Create a technical screening scorecard and interview questions for a [Senior AI Engineer] role."

AI terminology for HR

1. "Explain [LLMs, embeddings, vector databases, RAG, and AI agents] in simple terms for [new recruiters joining the team]."
2. "What do AI engineers mean by [inference, fine-tuning, and pre-training], and what skill levels are required for each?"
3. "What is the structural difference between the everyday work of [AI Engineering] and [Data Science/Analytics]?"
4. "What are [multimodal AI systems] and what skills are needed to build them?"
5. "Which AI terms are [meaningful skills] versus [overhyped buzzwords] that I should filter out on resumes?"

AI hiring insights

Junior AI Engineer

Common expectations:

• Python fundamentals
• Basic ML understanding
• API integration familiarity
• AI tooling awareness
• Basic experimentation skills

Mid-level AI Engineer

Common expectations:

• LLM workflow familiarity
• AI product integration experience
• Retrieval and vector database understanding
• Model evaluation awareness
• Backend and API integration skills

Senior AI Engineer

Common expectations:

• Production AI architecture design
• AI scalability and infrastructure understanding
• AI evaluation and observability expertise
• Cross-functional collaboration
• Mentoring and technical leadership
• AI product ownership

Staff / Lead AI Engineer

Common expectations:

• Organization-wide AI strategy
• AI infrastructure leadership
• Responsible AI governance
• AI platform architecture
• Cross-team AI enablement
• Long-term AI system planning

Important hiring realities

AI engineering is highly multidisciplinary

Strong AI Engineers often need:

• backend engineering skills
• infrastructure understanding
• data processing knowledge
• product thinking
• experimentation ability
• system design awareness

AI demos ≠ production AI expertise

A candidate may:

• build impressive AI demos
• but still lack:
  • scalability understanding
  • production reliability
  • AI evaluation maturity
  • observability practices
  • infrastructure knowledge

Prompt engineering alone is NOT enough

Strong AI professionals usually understand:

• retrieval systems
• embeddings
• orchestration
• evaluation
• APIs
• system architecture
• model limitations

rather than only writing prompts.

Strong AI engineers often think in systems

Strong candidates usually demonstrate:

• systems thinking
• experimentation maturity
• product reasoning
• scalability awareness
• AI safety awareness
• debugging ability
• operational thinking

rather than only model familiarity.

Common HR misunderstandings

AI Engineering ≠ Data Science

Data Science focuses more on:

• analysis
• experimentation
• statistics
• forecasting

AI Engineering focuses more on:

• production systems
• AI applications
• infrastructure
• deployment
• scalability

Generative AI ≠ all AI

Modern AI ecosystems also include:

• recommendation systems
• computer vision
• speech systems
• predictive analytics
• robotics
• autonomous systems

More AI buzzwords ≠ stronger AI candidate

Strong AI professionals usually demonstrate:

• production experience
• systems thinking
• evaluation maturity
• architecture understanding
• experimentation depth
• business reasoning

rather than only trending terminology.

Tips

• Senior AI engineers are often evaluated on scalability thinking, production maturity, evaluation practices, and system design capability rather than only model knowledge.
• AI portfolios are strongest when they demonstrate production thinking, evaluation workflows, and problem-solving depth rather than only simple chatbot demos.
• Many companies misuse AI titles — recruiters should clarify whether roles are ML-focused, LLM-focused, infrastructure-focused, research-focused, or product-focused.
• Avoid unrealistic job descriptions that expect a single AI engineer to simultaneously possess expert-level skills in research, DevOps, infrastructure, and product management.
• Modern AI teams operate in a highly cross-functional environment, collaborating closely with backend, data, security, product, and infrastructure teams.