Job Title: AI/ML Engineer
Job Purpose
The AI/ML Engineer Data Scientist is responsible for designing, developing, and deploying machine learning models, data pipelines, and intelligent AI agents that generate measurable business value across CNH Industrial. This role bridges classical data science with modern generative and agentic AI, enabling autonomous systems to plan, reason, and act on behalf of the business.
The role covers the full lifecycle of AI/ML solutions: from problem framing and data exploration, through model development and MLOps, to the design of multi-step AI agents that integrate with enterprise systems and execute complex tasks with minimal human intervention.
Leveraging a strong foundation in both statistics and software engineering, the senior holder of this role brings the maturity to mentor junior colleagues, contribute to the AI Center of Excellence, and drive responsible AI adoption aligned with CNH’s digital transformation strategy.
Collaboration happens within Agile/DevOps frameworks, using industry-leading MLOps tooling and cloud platforms.
Delivering production ML models for forecasting and optimization will directly reduce inventory costs and improve Supply Chain efficiency across AG and CE segments.
Agentic AI workflows will automate complex, multi-step manual processes, compressing cycle times and freeing human capacity for higher-value work.
Responsible, well-documented AI solutions will strengthen CNH’s data governance posture and accelerate enterprise adoption of AI at scale.
Key Responsibilities
- Design, build, and deploy end-to-end ML/AI solutions for key business use cases including demand forecasting, anomaly detection, predictive maintenance, NLP, and process optimization.
- Develop and maintain robust data pipelines for feature engineering, model training, evaluation, and production inference, using platforms such as Databricks and Azure ML.
- Design and implement agentic AI systems: autonomous agents capable of multi-step reasoning, tool use, memory, and goal-directed behavior using frameworks such as the Anthropic Claude Agent SDK, LangChain, LangGraph, AutoGen, or equivalent.
- Integrate AI agents with enterprise tools, databases, APIs, and services via MCP (Model Context Protocol) servers, function calling, and REST interfaces – enabling agents to take real actions within business workflows.
- Apply MLOps best practices: model versioning and registry (MLflow), CI/CD for ML pipelines, drift detection, A/B testing, and performance monitoring in production.
- Collaborate with business stakeholders to frame problems, define success metrics, and translate requirements into scalable AI/data solutions.
- Ensure responsible AI: model explainability, fairness and bias auditing, data privacy compliance, and alignment with CNH governance standards.
- Conduct code and model reviews; contribute to shared libraries, best practices, and the CNH AI Center of Excellence.
- Maintain comprehensive technical documentation including data dictionaries, model cards, agent architecture diagrams, and operational runbooks.
Experience Required
- 5+ years of hands-on experience in data science, machine learning, or AI engineering in complex enterprise environments.
- Must have experience in Supply chain domain.
- Strong proficiency in Python: pandas, NumPy, scikit-learn, PyTorch or TensorFlow/Keras
- Proven experience building, validating, and deploying ML models to production (batch and real-time inference)
- Experience with cloud platforms (Azure preferred, or AWS / GCP) and managed ML services (Azure ML, SageMaker, Vertex AI)
- Hands-on experience with Large Language Models (LLMs) and prompt engineering (Anthropic Claude, OpenAI GPT, or similar)
- Practical experience building AI agents or agentic workflows using frameworks such as Copilot Studio, AI Foundry, Claude Agent SDK, LangChain, LangGraph, AutoGen, or equivalent – including tool use, memory management, and multi-step planning
- Knowledge of MLOps practices: experiment tracking (MLflow), model monitoring, CI/CD for ML, data and concept drift detection
- Experience with SQL and big data / cloud warehouse platforms (Databricks, Snowflake, Azure Synapse, or similar)
- Experience in at least one industry SDLC tool (Jira / Azure DevOps), source code management (Git), and CI/CD pipelines
- Strong understanding of statistical methods, data structures, and algorithmic thinking
- Ability to translate business problems into data science formulations and communicate results to non-technical audiences
- A quality and responsibility mindset:
- Write clean, modular, well-documented, and testable code
- Value data accuracy, model transparency, and reproducibility
- Communicate proactively and collaborate across functions
- The ability to coach and mentor others
- Strong verbal, written and interpersonal skills (team player)
- Professional proficiency in English (verbal and written)
- Experience with MCP (Model Context Protocol) server development for agent tool integration
- Knowledge of RAG (Retrieval-Augmented Generation), vector databases, and embedding pipelines
- Experience with multi-agent orchestration and human-in-the-loop frameworks
- Familiarity with responsible AI
- Manufacturing, agriculture, or industrial domain knowledge
- Experience with graph databases or knowledge graphs for AI reasoning.
Preferred Qualifications
B.S. or M.S. in Computer Science, Data Science, Mathematics, Statistics, or a related field
5-10 years96
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Job Description
Download Resume
Job Title: AI/ML Engineer
Job Purpose
The AI/ML Engineer Data Scientist is responsible for designing, developing, and deploying machine learning models, data pipelines, and intelligent AI agents that generate measurable business value across CNH Industrial. This role bridges classical data science with modern generative and agentic AI, enabling autonomous systems to plan, reason, and act on behalf of the business.
The role covers the full lifecycle of AI/ML solutions: from problem framing and data exploration, through model development and MLOps, to the design of multi-step AI agents that integrate with enterprise systems and execute complex tasks with minimal human intervention.
Leveraging a strong foundation in both statistics and software engineering, the senior holder of this role brings the maturity to mentor junior colleagues, contribute to the AI Center of Excellence, and drive responsible AI adoption aligned with CNH’s digital transformation strategy.
Collaboration happens within Agile/DevOps frameworks, using industry-leading MLOps tooling and cloud platforms.
Delivering production ML models for forecasting and optimization will directly reduce inventory costs and improve Supply Chain efficiency across AG and CE segments.
Agentic AI workflows will automate complex, multi-step manual processes, compressing cycle times and freeing human capacity for higher-value work.
Responsible, well-documented AI solutions will strengthen CNH’s data governance posture and accelerate enterprise adoption of AI at scale.
Key Responsibilities
- Design, build, and deploy end-to-end ML/AI solutions for key business use cases including demand forecasting, anomaly detection, predictive maintenance, NLP, and process optimization.
- Develop and maintain robust data pipelines for feature engineering, model training, evaluation, and production inference, using platforms such as Databricks and Azure ML.
- Design and implement agentic AI systems: autonomous agents capable of multi-step reasoning, tool use, memory, and goal-directed behavior using frameworks such as the Anthropic Claude Agent SDK, LangChain, LangGraph, AutoGen, or equivalent.
- Integrate AI agents with enterprise tools, databases, APIs, and services via MCP (Model Context Protocol) servers, function calling, and REST interfaces – enabling agents to take real actions within business workflows.
- Apply MLOps best practices: model versioning and registry (MLflow), CI/CD for ML pipelines, drift detection, A/B testing, and performance monitoring in production.
- Collaborate with business stakeholders to frame problems, define success metrics, and translate requirements into scalable AI/data solutions.
- Ensure responsible AI: model explainability, fairness and bias auditing, data privacy compliance, and alignment with CNH governance standards.
- Conduct code and model reviews; contribute to shared libraries, best practices, and the CNH AI Center of Excellence.
- Maintain comprehensive technical documentation including data dictionaries, model cards, agent architecture diagrams, and operational runbooks.
Experience Required
- 5+ years of hands-on experience in data science, machine learning, or AI engineering in complex enterprise environments.
- Must have experience in Supply chain domain.
- Strong proficiency in Python: pandas, NumPy, scikit-learn, PyTorch or TensorFlow/Keras
- Proven experience building, validating, and deploying ML models to production (batch and real-time inference)
- Experience with cloud platforms (Azure preferred, or AWS / GCP) and managed ML services (Azure ML, SageMaker, Vertex AI)
- Hands-on experience with Large Language Models (LLMs) and prompt engineering (Anthropic Claude, OpenAI GPT, or similar)
- Practical experience building AI agents or agentic workflows using frameworks such as Copilot Studio, AI Foundry, Claude Agent SDK, LangChain, LangGraph, AutoGen, or equivalent – including tool use, memory management, and multi-step planning
- Knowledge of MLOps practices: experiment tracking (MLflow), model monitoring, CI/CD for ML, data and concept drift detection
- Experience with SQL and big data / cloud warehouse platforms (Databricks, Snowflake, Azure Synapse, or similar)
- Experience in at least one industry SDLC tool (Jira / Azure DevOps), source code management (Git), and CI/CD pipelines
- Strong understanding of statistical methods, data structures, and algorithmic thinking
- Ability to translate business problems into data science formulations and communicate results to non-technical audiences
- A quality and responsibility mindset:
- Write clean, modular, well-documented, and testable code
- Value data accuracy, model transparency, and reproducibility
- Communicate proactively and collaborate across functions
- The ability to coach and mentor others
- Strong verbal, written and interpersonal skills (team player)
- Professional proficiency in English (verbal and written)
- Experience with MCP (Model Context Protocol) server development for agent tool integration
- Knowledge of RAG (Retrieval-Augmented Generation), vector databases, and embedding pipelines
- Experience with multi-agent orchestration and human-in-the-loop frameworks
- Familiarity with responsible AI
- Manufacturing, agriculture, or industrial domain knowledge
- Experience with graph databases or knowledge graphs for AI reasoning.
Preferred Qualifications
B.S. or M.S. in Computer Science, Data Science, Mathematics, Statistics, or a related field