AI Engineering
MLOps Engineer
Models in production fail in stranger ways than models in notebooks ever could. The MLOps engineer is the person who anticipates those failures and builds the scaffolding that makes machine learning survive contact with real users.
Think feature stores that stay consistent between training and serving, deployment pipelines through MLflow that make rollbacks boring, and observability that catches drift before stakeholders notice. The work sits at the intersection of platform engineering and data science, and rewards people who like building tools other engineers will rely on.
A student grows into this role by getting comfortable with Kubernetes early and developing taste for what a healthy ML system actually looks like under load.
- CodeIntermediateNew
Automate Retraining with a Drift-Triggered MLflow Pipeline
Stand up the pipeline end to end with the team's existing stack (MLflow tracking + model registry, Airflow orchestration). Wire Evidently to compute weekly drift; when drift cro…
- Mlflow
- Airflow Dags
- Data Drift Detection
ML Engineering and Production ML - CodeIntermediateNew
Design Prompt Versioning and Observability for a Coding Assistant
You will (1) design a prompt-registry data model (versions, owners, environments, change log) and implement it in Postgres + a small Python SDK, (2) instrument the assistant to …
- Prompt Versioning
- Observability
- Pii Scrubbing
LLM Application Development - CodeIntermediateNew
Containerized Model Inference on Kubernetes for a Fintech
You receive a pre-trained credit-risk model (a LightGBM model file) and a sample request payload. Containerize a FastAPI inference service, deploy to EKS or GKE (a single-zone c…
- Kubernetes Orchestration
- Containerization
- Autoscaling
Cloud Computing for Data and ML - DesignIntermediateNew
Stand Up a Feature Store for a Series-B Fintech
Pick one priority feature group (recommend the 25 transaction-history features used by the fraud model). Define the offline source-of-truth (likely Snowflake or BigQuery), the o…
- Feature Store
- Feature Engineering
- Airflow Dags
ML Engineering and Production ML Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- DesignIntermediateNew
Instrument a Model Monitoring Stack from Scratch
Pick the priority product (recommend the customer-service RAG assistant, around 40k queries/day). Define monitoring signals: input drift (Evidently/NannyML), output quality (LLM…
- Model Monitoring
- Data Drift Detection
- LLM Evaluation
ML Engineering and Production ML - CodeIntermediateNew
Detect Change Points in a Trading Platform's Latency Telemetry
You receive 90 days of per-millisecond latency telemetry across 12 services, plus an incident log of 14 known regressions and 22 known false-alarm-class events. Implement and tu…
- Change Point Detection
- Anomaly Detection
- Time Series Analysis
Time Series Analysis and Forecasting - CodeIntermediateNew
Quantize a CNN for Battery-Powered Wildlife Cameras at a Climate Nonprofit
You receive an FP32 CNN (MobileNetV2 fine-tuned to 22 species, around 13 MB) and a hold-out test set of 4,000 images. Quantize to int8 (post-training quantization first, then qu…
- Quantization
- Qat
- Edge Deployment
Deep Learning - DesignIntermediateNew
Design a Continuous Eval Pipeline for an Enterprise RAG Product
Design (and partially build) a continuous-eval pipeline for a RAG system: (1) a structured eval set with at least 50 queries grouped by query class; (2) automated scoring (LLM-a…
- Continuous Evaluation
- LLM Evaluation
- RAG Architectures
AI Measurement and Evaluation - Browse challenges
Explore role
Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- AnalysisIntermediateNew
Benchmark Approximate Nearest-Neighbor Indexes for a Code-Search Startup
You receive a 5 M-vector sample (768-dim, float32) and a 1,000-query labeled benchmark with ground-truth top-50 neighbors per query. Index the same sample in Chroma (HNSW), Qdra…
- Ann Indexes
- Hnsw
- Benchmarking
Vector Databases and Embeddings - AnalysisIntermediateNew
Capstone Lab: Diagnose Why a Production Model Quietly Stopped Working
You receive 6 months of production logs (model inputs, predictions, ground truth from chargebacks) plus the original training data and model card. Reproduce the recall drop in a…
- Data Drift Detection
- Model Monitoring
- 5 Whys & Fishbone Root Cause Analysis
AI/ML Practicum and Hands-on Lab - CodeIntermediateNew
Build a Canary Rollout for a Production Recommender
Pick a serving stack (Triton, Seldon Core, KServe, or BentoML). Implement two-model traffic splitting with a configurable percentage (start at 5%). Wire up online metric collect…
- Canary Deployment
- Kubernetes Orchestration
- A/B Testing
ML Engineering and Production ML
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Related roles you may want to explore
Browse all roles →AI Engineering
AI Engineer
Between a promising research paper and a feature people actually use sits a long, unglamorous bridge — and AI engineers build it. The job is taking models that work in notebooks and shaping them into systems that hold up under real traffic, real costs, and real users with messy questions. Good work here looks like a retrieval pipeline that gets answers right ninety-something percent of the time, with evaluation harnesses catching regressions before they ship. Students grow into this role by treating Python and PyTorch as instruments rather than checkboxes, then learning how to reason about latency, evaluation, and cost together. If you enjoy stitching ideas into running software, this path will feel like home.
AI Engineering
Computer Vision Engineer
Teaching a machine to see is harder than it sounds and more interesting than it looks. Computer vision engineers shape the systems that read documents, navigate self-driving cars, screen medical images, and answer questions about photos. The role mixes the math of multi-view geometry with the engineering grind of getting models small and fast enough to run where they're needed — sometimes on a phone, sometimes on a robot. Good work here looks like a pipeline that holds up in real lighting, real motion, and real failure modes. Students grow into this path by getting hands-on with OpenCV and PyTorch early, then learning the harder craft of optimizing models without quietly destroying their accuracy.
AI Engineering
Machine Learning Engineer
A model that works on a laptop and a model that works for millions of users are two very different artifacts, and machine learning engineers live in the gap between them. The role exists to take research-grade ML and turn it into reliable production systems, which means caring about latency, retraining pipelines, and what happens when the data distribution shifts at three in the morning. Students grow into this through hands-on work with PyTorch or TensorFlow plus enough software engineering discipline to run real CI/CD. Tools like AWS SageMaker become part of the workflow. Strong ML engineers can talk shop with data scientists on one side and platform engineers on the other, and that bilingual quality is often what gets them hired.
AI Engineering
NLP Engineer
Language is messy. People misspell, contradict themselves, ask the same thing five different ways, and expect a machine to understand. NLP engineers build the systems that try. The role spans classical text processing in spaCy, modern retrieval-augmented architectures stitched together with LangChain, and the constant judgment calls about when to fine-tune, when to prompt, and when to fall back to rules. It rewards people who love both linguistics and systems thinking. Students grow into it through small projects — a question-answering bot over their notes, a classifier for their inbox — that surface the real failure modes of language models. Good NLP engineers obsess over evaluation as much as architecture.
AI Engineering
Prompt Engineer
Writing instructions for a model is a strange new craft. The words you choose, their order, the examples you include — all shape what a multi-billion-parameter system actually does next. Prompt engineers treat this as a real engineering discipline: versioning prompts in tools like PromptLayer, running evaluations across thousands of test cases, optimizing for cost and latency in production, and collaborating with domain experts to encode their judgment in text. The role is new enough that students often help define it on the job. Growing into it means building intuition for how models fail, when to fine-tune instead, and how to write specs precise enough to ship. Good prompt engineers measure everything and trust vibes only as a starting point.
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Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.
Skills and disciplines shown on this page are derived from the Ewance challenge catalogue. When the median annual salary is available for this role via Adzuna, it will be shown above with the sample size and country.
Portrait: Photo by Mario Klassen on Unsplash.