Case Study 2: Building a Career Roadmap in AI Engineering

Overview

Three AI engineers at different career stages -- a recent bootcamp graduate (Alex), a mid-career ML engineer transitioning from backend development (Jordan), and a senior data scientist moving into AI engineering (Sam) -- each build a personalized 12-month career roadmap. This case study follows their planning process, execution, and outcomes, illustrating how to navigate the rapidly evolving AI engineering landscape.

Problem Statement

Each engineer faces distinct challenges:

1. Alex (early career): Has Python proficiency and basic ML knowledge from a bootcamp. Needs to build depth in deep learning and production systems. Goal: land a junior ML engineer role.

2. Jordan (mid-career transition): Has 5 years of backend engineering experience (distributed systems, databases, APIs) and basic ML knowledge. Goal: transition into an ML infrastructure engineer role.

3. Sam (senior, pivoting): Has 8 years of data science experience (statistics, pandas, scikit-learn) and management experience. Goal: become an AI engineering lead at their current company.

The common challenge: the AI field evolves faster than any individual can learn. How do you prioritize what to learn, avoid hype-driven decisions, and build durable skills?

Approach

Step 1: Skills Gap Analysis

Each engineer maps their current skills against the "T-shaped" profile from Section 40.9:

Foundation layer (horizontal bar): - Mathematical foundations (linear algebra, probability, optimization) - Programming proficiency (Python, software engineering) - ML fundamentals (supervised, unsupervised, evaluation) - Deep learning (transformers, training, fine-tuning) - MLOps and deployment - Domain knowledge

Specialization (vertical stroke): - One or two deep areas of expertise

Step 2: Prioritization Framework

Each engineer scores skills on two axes: 1. Gap severity (1-5): How far is current skill from target? 2. Career impact (1-5): How much will this skill affect job prospects?

Priority = Gap severity * Career impact. Focus on the top 3-5 priorities.

Step 3: Learning Plan Execution

Each engineer creates a quarterly plan with specific milestones, following the deliberate practice principles from Section 40.9.

Results

Alex's Roadmap (Early Career)

Quarter 1: Deep Learning Foundations - Completed fast.ai course and PyTorch tutorials - Built 3 projects: image classifier, text classifier, fine-tuned BERT - Studied transformer architecture in depth (attention, positional encoding) - Milestone: Reproduced a simplified GPT-2 training run

Quarter 2: Production ML - Learned Docker, Kubernetes basics, CI/CD - Built an end-to-end ML pipeline: data processing -> training -> API serving - Contributed to an open-source ML project (documentation + bug fix) - Milestone: Deployed a model behind a FastAPI endpoint

Quarter 3: LLM Engineering - Studied RAG, prompt engineering, fine-tuning with LoRA - Built a RAG chatbot for a personal project (cooking recipes) - Learned evaluation methodologies (RAGAS, human evaluation) - Milestone: Published a blog post about RAG evaluation

Quarter 4: Job Search + Advanced Topics - Studied system design for ML (Chapter 33) - Practiced ML system design interviews - Explored reinforcement learning basics - Milestone: Received and accepted a junior ML engineer offer

Outcome: Alex landed a junior ML engineer role at a Series B startup, building RAG systems. The blog post was the single most impactful item in the job search, cited by two interviewers.

Jordan's Roadmap (Mid-Career Transition)

Quarter 1: ML Fundamentals Depth - Completed Stanford CS229 (online) for mathematical foundations - Studied PyTorch deeply: custom datasets, training loops, distributed training - Leveraged existing distributed systems knowledge to understand DDP and FSDP - Milestone: Trained a model on 4 GPUs using PyTorch DDP

Quarter 2: ML Infrastructure - Studied MLOps: experiment tracking (W&B), model serving (TorchServe, TGI) - Learned about GPU profiling, memory optimization, quantization - Built an internal tool for experiment comparison at current company - Milestone: Reduced model serving latency by 40% at work

Quarter 3: LLM Infrastructure - Studied inference optimization: KV caching, speculative decoding, vLLM - Learned about distributed inference for large models - Contributed to an open-source inference framework - Milestone: Deployed a production LLM serving pipeline

Quarter 4: Specialization + Transition - Deep dive into distributed training (FSDP, DeepSpeed) - Built a training framework prototype for their team - Published an internal tech talk on efficient LLM serving - Milestone: Transitioned to ML infrastructure engineer role (internal transfer)

Outcome: Jordan leveraged existing backend and distributed systems skills to transition into ML infrastructure. The key insight was that 70% of ML infrastructure work is classic systems engineering; the ML-specific knowledge filled the remaining 30%.

Sam's Roadmap (Senior, Pivoting)

Quarter 1: Deep Learning and Transformers - Moved from scikit-learn to PyTorch for daily work - Studied transformer architecture, attention mechanisms, scaling laws - Fine-tuned a model for an internal NLP task (replacing an older sklearn pipeline) - Milestone: Delivered 8% accuracy improvement using transformer model

Quarter 2: LLMs and Applications - Studied LLM capabilities, prompt engineering, RAG architecture - Led a cross-functional project to evaluate LLM integration opportunities - Built proof-of-concept for three internal use cases - Milestone: Secured executive buy-in for LLM pilot project

Quarter 3: AI Engineering Leadership - Studied AI safety, responsible AI, evaluation frameworks - Developed team evaluation standards for LLM-powered features - Mentored two junior engineers in deep learning - Milestone: Hired and onboarded two AI engineers for the pilot team

Quarter 4: Strategy and Scale - Developed AI engineering roadmap for the product org - Established MLOps practices: experiment tracking, monitoring, A/B testing - Presented AI strategy to C-suite - Milestone: Promoted to AI Engineering Lead

Outcome: Sam's management experience and domain knowledge were more valuable than expected. The technical upskilling (PyTorch, transformers, LLMs) was necessary but not sufficient -- the ability to connect AI capabilities to business outcomes was the differentiator.

Comparative Analysis

Key Lessons

1. The T-shaped profile is the most practical career framework. All three engineers benefited from broad awareness (reading widely, attending talks) combined with deep investment in 1-2 specializations. Trying to learn everything deeply leads to burnout and shallow expertise.

2. Build in public. Alex's blog post and Jordan's open-source contribution were disproportionately impactful. Public artifacts (code, writing, talks) create serendipitous opportunities and signal competence more effectively than credentials.

3. Leverage your existing strengths rather than starting from scratch. Jordan's systems engineering background made distributed training and ML infrastructure a natural fit. Sam's leadership experience enabled a faster transition to an AI engineering lead role than a purely technical path would have.

4. The AI field rewards learning agility over static knowledge. Specific frameworks and techniques become obsolete quickly, but the ability to rapidly learn new paradigms (by reading papers, running experiments, and building prototypes) is permanently valuable.

5. Hype resistance is a career skill. All three engineers encountered hype cycles during their 12-month journey. The engineers who maintained focus on their roadmap (while staying aware of developments) progressed faster than those who chased every new trend.

6. Internal transfers are underappreciated. Jordan and Sam both achieved their career transitions through internal moves. Existing organizational knowledge, relationships, and trust reduced the risk for both the engineer and the employer.

Code Reference

The career analysis and skills assessment tools are available in code/case-study-code.py.