Agenticbricks

Applied Scientist

Type: Full-time

About AgenticBricks

AgenticBricks is an AI-native engineering organization helping companies in healthcare, pharma, manufacturing, and supply chain navigate their AI journey. We work where software touches compliance surfaces, production lines, and patient datamodernizing legacy systems (EHR, LIMS, MES, ERP, WMS), embedding AI directly into the engineering process, and building custom SaaS platforms that survive audits and inspections.

Our AI-assisted approach isn't a chatbot layer bolted onto old workflows; it's AI used to analyze codebases, generate tests, enforce standards, and iterate through failures autonomously, delivering faster and with more rigor than traditional engineering. We invest in intellectually curious people, training them to become Claude Code Certified Senior AI Engineers who work directly with our team and embedded with our clients.

If you want to do serious AI-native engineering in regulated, high-stakes environments and grow at the frontier of AI-native development, we'd like to meet you.

The role

We are hiring an Applied Scientist at AgenticBricks supporting a large ecommerce retailer. You'll own the core ML lifecycle end to end feature engineering, building and training models in production, and inference at scale for systems that serve real customers and operations at major online retail volume. This is hands-on applied work: your output is production models and the pipelines that feed and serve them, not prototypes that stop at a notebook.

What you'll do

Feature engineering

• Design, build, and maintain features from large-scale, messy retail data transactions, catalog, behavioral signals, supply-chain and operational data.
• Build reliable feature pipelines (batch and streaming) and the transformations behind them, with an eye toward correctness, freshness, and reuse across models.
• Work with feature stores and data infrastructure so the same features are consistent between training and serving, and debug train/serve skew when it shows up.

Models built in production

• Train, validate, and productionize models against real production data and infrastructure not sandboxed datasets.
• Stand up reproducible training pipelines: versioned data and features, automated retraining, and the evaluation gates that decide what ships.
• Tune for the realities of scale and cost, and design the offline and online experiments (including A/B tests) that prove a model is actually better.

Inference

• Build and optimize model serving for production batch, real-time, and low-latency online inference under retail traffic loads.
• Own the operational side of inference: latency, throughput, cost, autoscaling, monitoring, and drift detection.
• Diagnose and fix production model issues quickly, and close the loop between what's observed in serving and what gets fixed in features or training.

What we're looking for

• Graduate degree in a quantitative field (ML, CS, statistics, applied math) or equivalent applied experience.
• Strong ML fundamentals plus the statistical literacy to evaluate models honestly.
• Strong programming skills (Python and the standard ML/data stack) and the ability to write production-quality code other engineers build on.
• Demonstrated experience taking models all the way to production feature pipelines, training, and serving at meaningful scale.
• Practical command of the full pipeline: feature engineering, reproducible training, and inference/serving, including the failure modes at each stage.
• Clear communication; you can explain a method, a result, and a caveat to a non-technical stakeholder.

Nice to have

• Experience in ecommerce, retail, marketplace, or large-scale consumer products.
• Hands-on work with feature stores, streaming pipelines, distributed training, or model-serving infrastructure.
• Familiarity with recommendation, search/ranking, or forecasting systems.
• MLOps depth: CI/CD for models, monitoring, versioning, and drift management in production.