Gen-AI Architect

About US

Johnson Electric is one of the worlds largest providers of motion solutions, supplying nearly every major brand in automotive, industrial, and consumer markets. Our Smart Factory initiative leverages advanced analytics, AI and IIoT to drive zero-defect quality, lights-out manufacturing, and sustainable operations across 30+ plants on five continents.

You will be joining a high-impact, hands-on CoE team that owns the full analytical stack: from edge data acquisition and cloud ingestion to model deployment and smart factory adoption.

Both roles will be cross-trained. Gen-AI specialists will learn industrial ML, and ML engineers will gain exposure to generative AI toolchains.

Gen-AI Architect

Own the end-to-end Gen AI technology stack for Johnson Electric. Design, build and govern reusable toolchains, unlock new use-cases and establish DevOps and MLOps best practices that can scale.

1) Problem framing + product sense

• Translate ambiguous business questions into measurable ML/data problems.
• Define success metrics, constraints (latency, cost, risk), and an evaluation plan.
• Decide when not to use ML and propose simpler alternatives.

2) Strong statistics + experimentation

• Hypothesis testing, confidence intervals, power/sample size, bias/variance.
• A/B testing design and pitfalls (multiple testing, peeking, novelty effects).
• Causal thinking basics (confounding, selection bias); at least one causal method in practice (e.g., matching, diff-in-diff, IV, uplift).

3) Solid ML fundamentals (applied)

• Supervised learning: linear/logistic regression, tree-based models, boosting, regularization, calibration.
• Model evaluation: proper metrics, cross-validation, leakage prevention, error analysis.
• Feature engineering, imbalanced learning, interpretability techniques.

4) Data fluency: SQL + data modeling

• Write complex SQL (joins, windows, CTEs), debug data issues, validate pipelines.
• Understand data schemas, grain, dimensional modeling basics, data quality checks.

5) Programming + reproducible workflows

• Proficiency in Python (or R) for data/ML work; ability to write production-quality notebooks/scripts.
• Code quality: modularity, testing basics, version control (Git), packaging/environment management.
• Clear documentation and reproducible analyses.

6) Communication + stakeholder management

• Explain tradeoffs and results to non-technical audiences; tell a coherent data story.
• Write concise decision docs; align on metrics and scope; handle pushback.

7) End-to-end delivery ownership

• Take a project from data discovery modeling validation launch plan monitoring.
• Identify risks early (data availability, privacy, feedback loops) and drive mitigation.

Good to have

1) Production ML / MLOps depth

• Model serving patterns (batch vs real-time), CI/CD, feature stores, model registry.
• Monitoring: data drift, performance decay, alerting; retraining strategies.
• Familiarity with Docker/Kubernetes and cloud ML stacks (AWS/GCP/Azure).

2) Deep learning (when relevant)

• Practical experience with PyTorch/TensorFlow, embeddings, transformers, training/inference tradeoffs.
• Domain-specific DL (NLP, CV, recommender systems).

3) Data engineering / big data tooling

• Spark/Databricks, distributed computing, orchestration (Airflow), streaming (Kafka).
• Building reliable datasets, backfills, incremental loads.

4) Advanced causal inference

• Causal graphs (DAGs), doubly robust estimators, synthetic controls, mediation, Bayesian causal.
• Designing quasi-experiments in messy real-world settings.

5) Optimization + decision science

• Linear/integer programming, bandits, reinforcement learning (select use cases).
• Forecasting at scale (hierarchical, probabilistic).

6) Domain expertise

• E.g., fraud/risk, ads/auction systems, supply chain, pricing, healthcare, fintech compliance.
• Knowing domain constraints often matters as much as algorithm choice.

7) Leadership beyond your own work

• Mentoring, setting best practices, reviewing others’ analyses/models.
• Influencing roadmap, creating reusable frameworks, raising the team’s bar.