Career Path: What AI Engineers Actually Do

— What does an AI engineer actually do all day? This article demystifies the role: real responsibilities, required skills, career progression, and how AI engineering differs from ML engineering and traditional software engineering.

level: fundamentals topics: career, mindset tags: career, roles, skills, ai-engineering

What “AI Engineer” Actually Means

“AI Engineer” is a new role that emerged around 2023-2024.

It is not:

  • Machine Learning Engineer (who trains models)
  • Data Scientist (who analyzes data)
  • Software Engineer who uses AI occasionally

It is:

  • Engineer who builds products and features powered by AI (usually LLMs)
  • Focuses on integration, application, and production systems
  • Works with pre-trained models, not training them

Analogy: Database Engineers do not build MySQL. They build systems that use databases effectively.

AI Engineers do not build GPT-4. They build systems that use LLMs effectively.

Day-to-Day Responsibilities

What AI Engineers Actually Do

40% of time: Prompt engineering and evaluation

  • Writing and refining prompts
  • Building evaluation sets
  • A/B testing different approaches
  • Measuring quality improvements

30% of time: Integration and architecture

  • API integration (OpenAI, Anthropic, etc.)
  • Building RAG pipelines
  • Designing fallback systems
  • Infrastructure and deployment

20% of time: Debugging and quality improvement

  • Investigating production failures
  • Improving error rates
  • Handling edge cases
  • Tuning parameters

10% of time: Monitoring and operations

  • Tracking latency and costs
  • Analyzing user feedback
  • Maintaining production systems
  • On-call and incident response

What AI Engineers rarely do:

  • Train models from scratch (<5% of roles do this)
  • Deep learning research (that is ML Researchers)
  • Data labeling (that is Data Annotators)
  • Writing model architectures (that is ML Engineers)

Key Skills and Technologies

Must-Have Skills

1. Software engineering fundamentals

  • APIs and HTTP
  • Databases and data modeling
  • System design and architecture
  • Version control (Git)
  • Testing and CI/CD

2. Prompt engineering

  • Crafting effective prompts
  • Few-shot learning
  • Chain-of-thought prompting
  • Output validation

3. LLM APIs and frameworks

  • OpenAI API, Anthropic API, etc.
  • LangChain or LlamaIndex (or alternatives)
  • Vector databases (Pinecone, Weaviate, Chroma)
  • Embeddings and semantic search

4. Evaluation and testing

  • Building eval sets
  • A/B testing
  • Statistical significance
  • Quality metrics (ROUGE, BLEU, custom metrics)

5. Production operations

  • Monitoring (Datadog, Prometheus, etc.)
  • Cost tracking and optimization
  • Latency optimization
  • Error handling and fallbacks

Nice-to-Have Skills

1. ML basics (but not deep expertise)

  • Understanding of embeddings
  • Familiarity with fine-tuning concepts
  • Basic knowledge of how models work
  • Do not need: Deep learning theory, model training, PyTorch/TensorFlow

2. Domain expertise

  • Depends on company (legal, medical, finance, etc.)
  • Understanding of use case requirements
  • Knowing when AI is appropriate vs not

3. Product and UX sense

  • Understanding user needs
  • Designing around AI limitations
  • Balancing quality vs latency vs cost

Technologies You Will Use

LLM Providers:

  • OpenAI (GPT-4, GPT-3.5)
  • Anthropic (Claude)
  • Google (Gemini)
  • Open-source (LLaMA, Mistral via HuggingFace or self-hosted)

Frameworks and Tools:

  • LangChain or LlamaIndex (orchestration)
  • Vector databases (Pinecone, Weaviate, Chroma, pgvector)
  • Prompt management (PromptLayer, Humanloop, or custom)
  • Evaluation tools (PromptFoo, custom frameworks)

Infrastructure:

  • Cloud platforms (AWS, GCP, Azure)
  • Containerization (Docker, Kubernetes)
  • Monitoring (Datadog, Grafana, custom dashboards)
  • Feature flags (LaunchDarkly, etc.)

Programming Languages:

  • Python (most common, 80%+ of roles)
  • JavaScript/TypeScript (for full-stack AI)
  • Go or Rust (for performance-critical components)

How AI Engineering Differs From Other Roles

AI Engineer vs Machine Learning Engineer

ML Engineer:

  • Trains models
  • Works with training data
  • Optimizes model architecture
  • Uses TensorFlow, PyTorch
  • Focuses on model performance metrics

AI Engineer:

  • Uses pre-trained models
  • Works with prompts and APIs
  • Optimizes integration and UX
  • Uses LLM APIs and orchestration frameworks
  • Focuses on product metrics

Overlap: Both need software engineering skills, understanding of ML concepts.

Key difference: ML Engineers build the models. AI Engineers build products with the models.

AI Engineer vs Software Engineer

Software Engineer:

  • Builds features with deterministic logic
  • Focuses on correctness and reliability
  • Uses traditional frameworks and databases

AI Engineer:

  • Builds features with probabilistic AI
  • Focuses on acceptable error rates
  • Uses AI APIs and specialized tools

Overlap: Both need strong software engineering fundamentals.

Key difference: AI Engineers work with non-deterministic systems and embrace probabilistic thinking.

AI Engineer vs Data Scientist

Data Scientist:

  • Analyzes data to find insights
  • Builds statistical models
  • Creates dashboards and reports
  • Uses SQL, pandas, Jupyter notebooks

AI Engineer:

  • Builds production AI features
  • Integrates AI into applications
  • Ships code to production
  • Uses APIs, frameworks, deployment tools

Overlap: Both need statistical thinking and data handling skills.

Key difference: Data Scientists analyze. AI Engineers build and ship.

Career Levels and Progression

Junior AI Engineer (0-2 years AI experience)

Responsibilities:

  • Implement prompts based on specs
  • Build evaluation sets
  • Integrate LLM APIs into existing systems
  • Fix bugs and edge cases

Expected skills:

  • Basic prompt engineering
  • API integration
  • Testing and debugging
  • Understanding of LLM fundamentals

Typical projects:

  • “Integrate GPT-4 for summarization feature”
  • “Build RAG pipeline for documentation search”
  • “Create eval set for customer support chatbot”

Compensation (US, 2024-2026):

  • $100K-$150K base
  • Equity varies by company

Mid-Level AI Engineer (2-5 years)

Responsibilities:

  • Design AI features end-to-end
  • Choose appropriate models and architectures
  • Optimize cost and latency
  • Mentor junior engineers

Expected skills:

  • Advanced prompt engineering
  • System design for AI
  • A/B testing and experimentation
  • Production operations

Typical projects:

  • “Design and implement multi-agent customer support system”
  • “Migrate from GPT-3.5 to self-hosted model to reduce costs”
  • “Build evaluation framework for entire product”

Compensation:

  • $150K-$220K base
  • Significant equity

Senior AI Engineer (5-10 years software, 3+ years AI)

Responsibilities:

  • Architect AI infrastructure
  • Make model and vendor selection decisions
  • Lead AI initiatives across teams
  • Define best practices

Expected skills:

  • Expert prompt engineering and evaluation
  • Deep system design expertise
  • Strategic thinking (build vs buy, cost optimization)
  • Technical leadership

Typical projects:

  • “Architect company-wide AI platform”
  • “Evaluate and select AI vendors for 3-year contract”
  • “Design fallback and redundancy systems”

Compensation:

  • $180K-$300K+ base
  • Large equity grants

Staff/Principal AI Engineer (10+ years)

Responsibilities:

  • Company-wide AI strategy
  • Cross-functional collaboration with product, ML, and infrastructure
  • Setting technical direction
  • Representing AI engineering in leadership

Expected skills:

  • All of the above
  • Business and product strategy
  • Organizational influence
  • Deep technical expertise

Typical projects:

  • “Define AI engineering roadmap for next 2 years”
  • “Build vs buy assessment for LLM infrastructure”
  • “Hire and build AI engineering team”

Compensation:

  • $250K-$500K+ base
  • Large equity

Types of Companies Hiring AI Engineers

1. AI-Native Startups

Examples: ChatGPT wrappers, AI-powered SaaS, new AI products

What you will do:

  • Core product is AI
  • Fast-paced experimentation
  • Lots of prompt engineering
  • High impact, high uncertainty

Best for:

  • Engineers who want to work on cutting-edge AI
  • Tolerance for ambiguity and rapid change
  • Willing to take equity risk

2. Established Tech Companies (Adding AI Features)

Examples: Google, Meta, Microsoft, Amazon, Airbnb, Stripe

What you will do:

  • Integrate AI into existing products
  • Work with large-scale systems
  • Balance innovation with reliability
  • Collaborate with ML teams

Best for:

  • Engineers who want stability + AI
  • Working with proven products
  • Learning from experienced teams

3. Traditional Companies (AI Transformation)

Examples: Banks, healthcare, retail, manufacturing

What you will do:

  • Bring AI to legacy systems
  • Educate organization about AI
  • Navigate compliance and regulation
  • Slower pace, higher stakes

Best for:

  • Engineers who want to drive transformation
  • Domain expertise (finance, healthcare, etc.)
  • Preference for stability over hyper-growth

4. AI Infrastructure/Platform Companies

Examples: OpenAI, Anthropic, HuggingFace, LangChain

What you will do:

  • Build tools for other AI engineers
  • Work on frameworks and platforms
  • Deep technical challenges
  • Influence the ecosystem

Best for:

  • Engineers who love developer tools
  • Want to shape the AI ecosystem
  • Enjoy infrastructure challenges

Skills Gap: What Employers Struggle to Find

High Demand, Low Supply

Skills employers desperately want:

  1. Prompt engineering at scale

    • Not just writing prompts, but building systems around them
    • Few engineers have production experience yet

  2. AI system design

    • Fallback hierarchies, evaluation frameworks
    • Combining AI with traditional logic
    • New domain, limited best practices

  3. Cost optimization

    • LLM costs can spiral quickly
    • Engineers who can optimize without sacrificing quality

  4. Production AI operations

    • Monitoring, debugging, incident response
    • Most engineers only have prototype experience

  5. Cross-functional collaboration

    • Explaining AI to non-technical stakeholders
    • Setting realistic expectations

Opportunity: If you develop these skills, you are highly valuable.

How to Break Into AI Engineering

From Software Engineering

You already have:

  • Programming and system design
  • APIs and databases
  • Testing and deployment

You need to learn:

  • LLM APIs and frameworks
  • Prompt engineering
  • Evaluation methodologies
  • Cost and latency optimization

Path:

  1. Build side project using LLM API (2-4 weeks)
  2. Read prompt engineering guides
  3. Experiment with evaluation sets
  4. Contribute to open-source AI tools
  5. Apply for AI engineer roles

Timeline: 3-6 months to be job-ready

From ML Engineering

You already have:

  • Understanding of models
  • Experience with embeddings
  • Statistical thinking

You need to learn:

  • Production software engineering
  • API integration (vs model training)
  • Prompt engineering
  • Product and UX considerations

Path:

  1. Build production AI application (not just notebook)
  2. Learn software engineering best practices
  3. Focus on API-based AI, not training
  4. Build portfolio of shipped projects

Timeline: 3-6 months to transition

From Data Science

You already have:

  • Data manipulation
  • Statistical analysis
  • SQL and data pipelines

You need to learn:

  • Software engineering (testing, deployment, APIs)
  • LLM APIs and prompt engineering
  • Production systems and reliability

Path:

  1. Learn Python beyond notebooks (packaging, testing)
  2. Build web application with AI feature
  3. Study system design
  4. Build production-quality projects

Timeline: 6-12 months to transition

Common Career Questions

”Do I need a PhD?”

No. AI Engineering is about application, not research.

  • PhDs are common in ML Engineering (model training)
  • Rare in AI Engineering (model application)
  • Practical experience matters more than academic credentials

”Do I need to know deep learning?”

Basic understanding: Yes. Deep expertise: No.

You should understand:

  • What embeddings are
  • Conceptually how transformers work
  • Fine-tuning at a high level

You do not need to:

  • Implement backpropagation
  • Understand transformer architecture deeply
  • Train models from scratch

”Is AI engineering a fad?”

No. It is a lasting shift.

AI integration will be part of software engineering permanently, just like:

  • Web development in 2000s
  • Mobile development in 2010s
  • Cloud infrastructure in 2010s-2020s

AI Engineering is the infrastructure layer for the AI era.

”What is the salary ceiling?”

Currently very high due to demand/supply imbalance.

  • Senior AI Engineers: $200K-$400K
  • Staff/Principal: $300K-$600K+
  • Leadership (VP of AI Engineering): $500K-$1M+

Expect these to normalize over 5-10 years as supply increases.

”Can I work remotely?”

Yes, most AI engineering roles are remote-friendly.

  • AI-native startups: Often fully remote
  • Tech companies: Hybrid or remote
  • Traditional companies: More likely on-site

Future of AI Engineering

Short-Term (1-3 years)

Expected changes:

  • Role becomes more standardized
  • Best practices emerge
  • More training resources available
  • Salaries normalize somewhat

Still needed:

  • Prompt engineering (not going away)
  • System integration
  • Production operations
  • Cost optimization

Medium-Term (3-7 years)

Possible changes:

  • Models get better (easier to use)
  • Tools get better (less manual work)
  • More specialization (AI UX engineer, AI reliability engineer, etc.)

Still needed:

  • Strategic AI decisions
  • Evaluation and quality control
  • Handling edge cases
  • System design

Long-Term (7-10 years)

Uncertain, but likely:

  • AI engineering skills merge back into general software engineering
  • All engineers expected to use AI
  • Specialization in AI-heavy domains (healthcare AI, legal AI, etc.)

Core skills that will always matter:

  • System design
  • Product thinking
  • Quality judgment
  • Continuous learning

Key Takeaways

  1. AI Engineers use pre-trained models, they do not train them
  2. 40% of work is prompt engineering and evaluation, not coding
  3. Must-have: Software engineering fundamentals + LLM APIs + prompt engineering
  4. Different from ML Engineering – application vs training
  5. High demand, low supply – great career opportunity now
  6. Do not need PhD – practical skills matter more
  7. Remote-friendly – most roles offer remote or hybrid
  8. Salaries are high – $150K-$300K+ for mid to senior levels
  9. Transition paths exist from SWE, MLE, and Data Science
  10. Long-term viable – not a fad, foundational shift

AI Engineering is a real career path with growing demand. If you are a software engineer who likes working with cutting-edge tech, probabilistic systems, and rapid iteration, this might be the right path for you.