AI Engineering

Write Python programs using variables, control flow, functions, classes, modules, files, exceptions, and common scientific-computing libraries.

Use the command line, Git, virtual environments, package managers, notebooks, debuggers, and code editors to create reproducible projects.

Work with arrays, lists, maps, trees, graphs, queues, and common algorithms while reasoning about runtime and memory use.

Structure maintainable applications with modules, interfaces, tests, logging, configuration, documentation, and code review.

Build and consume HTTP APIs, validate requests, handle authentication and failures, and connect AI components to web applications and services.

Use vectors, matrices, tensors, projections, similarity measures, and matrix operations that power modern machine learning.

Apply derivatives, gradients, loss functions, and numerical optimization to see how models improve their parameters.

Reason with random variables, distributions, conditional probability, Bayes’ rule, expectation, and uncertainty.

Use sampling, estimation, confidence intervals, hypothesis tests, experimental design, and practical measures of uncertainty.

Design tables, write SQL queries, join datasets, define indexes, and choose reliable ways to store structured information.

Load, clean, transform, visualize, and validate tabular, text, image, and event data without silently corrupting it.

Trace AI from symbolic reasoning and early neural networks through expert systems, statistical learning, deep learning, transformers, and foundation models. Connect each era to the data, hardware, institutions, and evaluation practices that shaped it.

Turn a product request into a measurable AI task by defining users, constraints, baselines, success metrics, failure costs, and non-AI alternatives.

Plan data sources, sampling, annotation instructions, quality checks, dataset versions, consent, retention, and representative coverage.

Implement state-space search, heuristic search, game search, constraint satisfaction, and planning for problems with explicit goals and rules.

Represent facts and relationships with rules, ontologies, and knowledge graphs, then perform logical or graph-based reasoning over them.

Model uncertain relationships with Bayesian networks, hidden-state models, and probabilistic inference techniques.

Train regression, classification, tree-based, kernel, and ensemble models, choosing methods that fit the data and operational constraints.

Create honest train, validation, and test splits; select metrics; detect leakage; tune models; estimate uncertainty; and compare against meaningful baselines.

Transform raw inputs into useful signals, handle missing values and categories, select features, and build leakage-safe preprocessing pipelines.

Use clustering, dimensionality reduction, anomaly detection, and representation methods when reliable labels are unavailable.

Model trend, seasonality, changing behavior, and delayed outcomes with forecasting methods and time-aware validation.

Build candidate generation, ranking, collaborative filtering, and content-based recommenders while measuring relevance, diversity, novelty, and feedback effects.

Separate prediction from cause-and-effect reasoning using causal graphs, experiments, matching, adjustment, and treatment-effect estimation.

Formulate states, actions, rewards, and policies; implement value-based and policy-based methods; and evaluate agents without unsafe trial and error.

Construct neural networks with tensors, layers, activations, losses, backpropagation, and automatic differentiation.

Train deep models with regularization, normalization, initialization, learning-rate schedules, checkpoints, and systematic debugging.

Build image classification, detection, segmentation, and visual embedding systems while handling augmentation, labeling, and visual failure modes.

Process and model text with tokenization, language modeling, classification, sequence labeling, retrieval, and generation.

Create systems for audio classification, speech recognition, speaker processing, and speech synthesis, with attention to noise and latency.

Implement attention, positional information, encoder and decoder architectures, pretraining objectives, and efficient transformer inference.

Work with tokenization, pretraining, instruction tuning, context windows, sampling, scaling behavior, and the capabilities and limits of large language models.

Design prompts with clear instructions, examples, constraints, context, and output schemas; test prompt changes rather than relying on anecdotes.

Integrate hosted and open-weight models using APIs and local runtimes, with streaming, retries, batching, rate limits, model routing, and fallback behavior.

Create semantic representations, measure similarity, build vector indexes, combine semantic and keyword retrieval, and evaluate search quality.

Build retrieval-augmented generation pipelines from document ingestion through chunking, indexing, retrieval, reranking, citation, generation, and evaluation.

Adapt foundation models with supervised fine-tuning, parameter-efficient methods, preference data, and careful train-test separation.

Build agents that select tools, maintain state, plan work, recover from errors, and operate within explicit permissions and stopping rules.

Combine text, images, audio, and video in multimodal models, and design applications that align or generate across those formats.

Use diffusion and related generative methods for image, audio, and video creation, including conditioning, guidance, sampling, and output evaluation.

Generate or augment training and test data while measuring realism, coverage, privacy leakage, bias, and whether synthetic examples improve real-world performance.

Connect perception, localization, planning, control, and safety checks in robots and other autonomous systems.

Evaluate generated outputs with task-specific test sets, model-based graders, human review, pairwise comparisons, red-team cases, and statistical reporting.

Design interfaces that communicate uncertainty, support review and correction, preserve user control, and decide when automation should hand work to a person.

Build batch and streaming pipelines for ingestion, transformation, validation, lineage, dataset versioning, and reusable model features.

Track code, data, parameters, environments, artifacts, and results so experiments can be compared, reproduced, and audited.

Use GPUs and accelerators effectively through tensor layouts, memory management, mixed precision, batching, parallelism, and distributed training.

Reduce memory, latency, and energy use with quantization, pruning, distillation, compilation, caching, and efficient model architectures.

Package models behind reliable batch, online, and streaming interfaces with versioning, autoscaling, concurrency control, and latency targets.

Package workloads with containers and run them on cloud services, Kubernetes, serverless platforms, or managed AI systems while handling identity, storage, networking, and scaling.

Automate tests, data checks, training, evaluation gates, registry promotion, deployment, rollback, and approval workflows.

Monitor input drift, output quality, latency, cost, retrieval behavior, user feedback, and model versions across conventional and generative AI systems.

Defend against adversarial examples, prompt injection, data poisoning, model theft, unsafe tool use, insecure dependencies, and sensitive-data exposure.

Apply data minimization, access controls, de-identification, differential privacy, federated learning, retention rules, and privacy testing.

Identify harmful capabilities and outputs, create safety policies and evaluations, add layered safeguards, and preserve meaningful human oversight.

Document datasets and models, assess bias and impact, manage approvals and accountability, and map systems to applicable AI, consumer, copyright, and sector rules.

Deploy models to phones, browsers, embedded devices, and edge hardware under tight limits on memory, power, connectivity, and response time.

Set service objectives, design fallbacks and circuit breakers, investigate failures, contain incidents, communicate impact, and prevent recurrence.

Estimate training and inference costs, choose models by value rather than size, control token and accelerator use, and measure energy and infrastructure tradeoffs.

Use AI coding assistants for implementation, tests, documentation, debugging, and review while verifying generated code, protecting secrets, and preserving engineering judgment.

Take an AI product from stakeholder request through data work, baseline selection, model development, evaluation, deployment, monitoring, incident planning, documentation, and post-launch improvement.

Map roles such as AI engineer, ML engineer, data scientist, research engineer, evaluation specialist, and AI product engineer. Build proof-of-skill projects, choose useful certifications when needed, contribute to open work, prepare for interviews, and stay current through papers, benchmarks, standards, and practitioner communities.