Rockwell Automation is a global technology leader focused on helping the world’s manufacturers be more productive, sustainable, and agile. With more than 28,000 employees who make the world better every day, we know we have something special. Behind our customers - amazing companies that help feed the world, provide life-saving medicine on a global scale, and focus on clean water and green mobility - our people are energized problem solvers that take pride in how the work we do changes the world for the better.
We welcome all makers, forward thinkers, and problem solvers who are looking for a place to do their best work. And if that’s you we would love to have you join us!
Job Description
Position Summary
We are looking for a experienced AI/ML Engineer to design, build, and deploy machine learning models and intelligent systems that solve real business problems at scale. This role has a strong focus on modern generative AI — building LLM-powered applications, RAG pipelines, and agentic AI systems — alongside classical ML. You will work across the full ML/AI lifecycle, from data exploration and model development to production deployment and monitoring, partnering closely with Data Science, Product, and Platform Engineering teams. This role suits someone who enjoys turning research and prototypes into reliable, production-grade systems.
You will be reporting to Team Lead-IT
Your Responsibilities
- Design and build LLM-powered applications, RAG pipelines, and agentic AI workflows to automate and augment business processes.
- Stay current with the latest ML/AI research (including LLMs, RAG, and agentic systems) and evaluate applicability to company use cases.
- Document architectures, experiments, and decisions; contribute to internal ML standards, code reviews, and knowledge sharing.
- Ensure responsible AI practices — fairness, explainability, privacy, and security — are embedded throughout model development
- Build and maintain robust, scalable data pipelines for feature engineering, model training, and inference (batch and real-time).
- Deploy models into production using MLOps best practices — CI/CD for ML, containerization, model versioning, and automated retraining pipelines.
- Monitor deployed models for drift, performance degradation, bias, and data quality issues; implement retraining and alerting strategies.
- Collaborate with Data Engineers to ensure clean, reliable, and well-governed data is available for model development.
- Partner with Product Managers and stakeholders to translate business requirements into ML solutions and model capabilities, limitations, and trade-offs.
- Conduct experiments (A/B tests, offline evaluation) to validate model performance and business impact.
- Optimize models for latency, throughput, and cost (quantization, distillation, pruning, efficient inference serving).
The Essentials – You Will Have
- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Machine Learning, or a related field (or equivalent practical experience).
- 2–5+ years of hands-on experience building and deploying machine learning models in production.
Technical Skills
- Strong programming skills in Python; familiarity with SQL
- Proficiency with ML/DL frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost/LightGBM.
- 3+ years of experience with the ML lifecycle: data preprocessing, feature engineering, model training, hyperparameter tuning, evaluation, and deployment.
- Hands-on experience with MLOps tooling: MLflow, Kubeflow, SageMaker, Vertex AI, Airflow, or similar for experiment tracking, orchestration, and deployment.
- Solid understanding of statistics, probability, linear algebra, and core ML/DL algorithms.
- Experience with cloud platforms (Azure) and containerization/orchestration (Docker, Kubernetes).
- Familiarity with distributed data processing (Spark, Ray, Dask) and data pipeline/orchestration tools (Airflow, Dagster).
- 3+ years of experience with model serving and APIs (FastAPI/Flask, TorchServe, Triton Inference Server, or similar).
- Version control (Git) and CI/CD practices for ML systems.
Generative AI, LLMs & Agentic Applications (Essential)
- Hands-on experience building applications powered by Large Language Models (LLMs) — e.g., OpenAI, Anthropic Claude, Llama, Mistral, or Gemini APIs.
- Practical experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines, including chunking strategies, embeddings, and retrieval evaluation.
- Experience building agentic AI systems / multi-step autonomous workflows (tool use, function calling, planning, orchestration) using frameworks such as LangChain, LlamaIndex, or similar.
- Working knowledge of vector databases (Pinecone, Weaviate, FAISS, Milvus, Chroma) for semantic search and retrieval at scale.
- Experience with prompt engineering, evaluation, and fine-tuning/adapting LLMs (LoRA, PEFT) for domain-specific use cases.
- Understanding of the trade-offs, latency, cost, and safety considerations of deploying LLM- and agent-based applications in production.
The Preferred – You Might Also Have
- Experience deploying and scaling multi-agent systems in production (orchestration, memory, guardrails, observability for agentic workflows).
- Contributions to open-source LLM/RAG tooling, or published work on GenAI applications.
- Publications, open-source contributions, or Kaggle competition experience.
- Experience with real-time/streaming ML systems (Kafka, Flink, Spark Streaming).
- Domain experience relevant to the business (e.g., fintech, healthcare, e-commerce, cybersecurity).
- Understanding of responsible AI, model interpretability (SHAP, LIME), and privacy-preserving ML techniques.
Tools & Technology (Representative)
- Languages: Python, SQL
- ML/DL: PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers
- GenAI / LLM: OpenAI, Anthropic Claude, LangChain, LlamaIndex, RAG pipelines, vector DBs (Pinecone/Weaviate/FAISS)
- MLOps: MLflow, Kubeflow, SageMaker/Vertex AI/Azure ML, Airflow
- Data: Spark, Pandas, Snowflake/BigQuery/Redshift
- Infra: Docker, Kubernetes, AWS/GCP/Azure, Terraform
- Monitoring: Prometheus/Grafana, Evidently AI, WhyLabs.
What We Offer:
Our benefits package includes:
- Comprehensive mindfulness programmed with a premium membership to Calm
- Volunteer Paid Time Off available after 6 months of employment
- Company volunteer and donation matching programme
- Employee Assistance Program
- Personalised wellbeing programmed through our OnTrack programmed
- On-demand digital course library for professional development
- ... and other local benefits!
At Rockwell Automation, we are dedicated to building a diverse, inclusive, and authentic workplace. If you're excited about this role but your experience doesn't align perfectly with every qualification, we encourage you to apply. You may be just the right person for this or other roles.
#LI-Hybrid
#LI-KV1
Rockwell Automation’s hybrid policy aligns that employees are expected to work at a Rockwell location at least Mondays, Tuesdays, and Thursdays unless they have a business obligation out of the office.
Skills Required
- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Machine Learning, or a related field, or equivalent practical experience
- 2-5+ years of hands-on experience building and deploying machine learning models in production
- Strong Python programming skills and familiarity with SQL
- Proficiency with TensorFlow, PyTorch, scikit-learn, XGBoost, or LightGBM
- 5+ years of experience with the machine learning lifecycle, including preprocessing, feature engineering, training, tuning, evaluation, and deployment
- Experience with MLOps tooling such as MLflow, Kubeflow, SageMaker, Vertex AI, or Airflow
- Understanding of statistics, probability, linear algebra, and core machine learning and deep learning algorithms
- Experience with Azure and Docker or Kubernetes
- Familiarity with Spark, Ray, or Dask and data orchestration tools such as Airflow or Dagster
- 5+ years of experience with model serving and APIs such as FastAPI, Flask, TorchServe, or Triton Inference Server
- Experience with Git and CI/CD practices for machine learning systems
- Hands-on experience building applications powered by large language models
- Practical experience designing and implementing retrieval-augmented generation pipelines
- Experience building agentic AI systems or multi-step autonomous workflows
- Working knowledge of vector databases such as Pinecone, Weaviate, FAISS, Milvus, or Chroma
- Experience with prompt engineering, evaluation, and LLM fine-tuning or adaptation using LoRA or PEFT
- Understanding of latency, cost, safety, and production deployment considerations for LLM and agent-based applications
- Experience deploying and scaling multi-agent systems in production
- Open-source contributions or published work related to generative AI applications
- Publications, open-source contributions, or Kaggle competition experience
- Experience with real-time or streaming machine learning systems using Kafka, Flink, or Spark Streaming
- Relevant domain experience in fintech, healthcare, e-commerce, or cybersecurity
- Understanding of responsible AI, model interpretability, and privacy-preserving machine learning
Rockwell Automation Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Rockwell Automation and has not been reviewed or approved by Rockwell Automation.
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Healthcare Strength — Healthcare coverage is described as comprehensive, spanning medical, dental, vision, mental‑health support, disability and life insurance, and wellness resources. Multiple plan choices and supportive programs contribute to the package feeling well‑rounded.
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Retirement Support — Retirement benefits include a 401(k)/Retirement Savings Plan with employer matching that is positioned as a meaningful part of total compensation. These offerings reinforce longer‑term financial security as a core strength.
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Parental & Family Support — Paid parental leave was expanded and a paid caregiver leave was added, indicating clear support for family needs. These policies are complemented by flexibility signals and dedicated paid volunteer time.
Rockwell Automation Insights
What We Do
At Rockwell Automation, we connect the imaginations of people with the potential of technology to expand what is humanly possible, making the world more intelligent, more connected and more productive.
