Senior AI Engineer

Job Summary:
Responsible for building high-quality, innovative and fully performing software in compliance with coding standards and technical design. Design, modify, develop, write and implement software programming applications. Support and/or install software applications. Key participant in the testing process through test review and analysis, test witnessing and certification of software.
Key Responsibilities:
Develop software solutions by studying information needs; conferring with users; studying systems flow, data usage and work processes; investigating problem areas; following the software development lifecycle; Document and demonstrate solutions; Develops flow charts, layouts and documentation Determine feasibility by evaluating analysis, problem definition, requirements, solution development and proposed solutions; Understand business needs and know how to create the tools to manage them Prepare and install solutions by determining and designing system specifications, standards and programming Recommend state-of-the-art development tools, programming techniques and computing equipment; participate in educational opportunities; read professional publications; maintain personal networks; participate in professional organizations; remain passionate about great technologies, especially open source Provide information by collecting, analyzing, and summarizing development and issues while protecting IT assets by keeping information confidential; Improve applications by conducting systems analysis recommending changes in policies and procedures Define applications and their interfaces, allocate responsibilities to applications, understand solution deployment, and communicate requirements for interactions with solution context, define Nonfunctional Requirements (NFRs) Understands multiple architectures and how to apply architecture to solutions; understands programming and testing standards; understands industry standards for traditional and agile development Provide oversight and foster Built-In Quality and Team and Technical Agility; Adopt new mindsets and habits in how people approach their work while supporting decentralized decision making.
Role Summary: The AI Engineer will design, build, validate, and deploy AI-enabled solutions that help Cummins teams convert complex operating, product, engineering, and business data into practical recommendations and intelligent workflows. This role will work closely with product owners, data scientists, data engineers, architects, and business stakeholders to deliver reusable AI capabilities across the Analytics and AI portfolio.
Skills
Strong analytical thinking and problem-solving skills with the ability to design AI solutions for complex business and technical challenges.
Ability to bridge software engineering fundamentals with modern AI/GenAI technologies to deliver production-ready solutions.
Effective communication skills with the ability to explain AI concepts, architecture decisions, and technical recommendations to both technical and non-technical stakeholders.
Customer-focused mindset with the ability to understand business problems and translate them into scalable AI applications.
Strong technical leadership, mentoring, and decision-making capabilities in fast-paced and evolving technology environments.
Technical Skills
AI Engineering & Generative AI
Strong experience building, deploying, and maintaining AI-powered applications and intelligent agents.
Expertise in Large Language Model (LLM) application development and Generative AI solution design.
Experience developing agentic AI workflows, multi-agent systems, and AI orchestration frameworks.
Knowledge of prompt engineering, agent evaluation, guardrails, grounding techniques, and AI application observability.
Familiarity with Retrieval-Augmented Generation (RAG), semantic search, vector databases, and knowledge management architectures.
Open Source AI Frameworks
Hands-on experience with modern AI orchestration frameworks including LangGraph, LangChain, LlamaIndex Hugging Face, Open-Source LLM ecosystems
Experience integrating both commercial and open-source foundation models.
Knowledge of model serving, inference optimization, and AI workflow orchestration patterns.
Software Engineering & Application Development
Strong proficiency in Python with experience building production-grade applications and APIs.
Experience with modern software engineering practices including object-oriented design, design patterns, testing, and secure coding principles.
Experience developing REST APIs, microservices, and event-driven architectures.
Familiarity with front-end technologies such as React, Angular, or similar frameworks for AI application development.
Understanding of distributed systems and scalable application architecture.
Data Engineering & AI Platforms
Experience working with Azure Databricks, Spark, and modern data platforms supporting AI workloads.
Strong understanding of data pipelines, feature engineering, and data preparation for AI applications.
Experience integrating enterprise data sources, structured and unstructured datasets, and knowledge repositories.
Familiarity with vector databases and embedding-based retrieval architectures.
Understanding of modern data lake, warehouse, and lakehouse architectures.
Cloud, DevOps & MLOps
Strong experience with Azure cloud services supporting AI workloads, including:
Azure AI Services
Azure OpenAI
Azure Databricks
Azure Functions
Azure Kubernetes Service (AKS)
Azure Storage and Data Services
Experience with Docker, Kubernetes, and cloud-native application deployment.
Familiarity with MLOps and LLMOps practices including model lifecycle management, monitoring, and continuous deployment.
Experience implementing CI/CD pipelines using Git, Azure DevOps, Jenkins, or equivalent platforms.
Knowledge of AI governance, security, responsible AI, and compliance practices.
AI Solution Architecture
Experience designing enterprise-scale AI platforms and reusable AI services.
Ability to evaluate AI technologies and recommend architecture patterns aligned with business objectives.
Knowledge of AI system performance optimization, scalability, reliability, and operational excellence.
Experience establishing engineering standards, reusable frameworks, and best practices for AI solution delivery.
Experience
Minimum 8+ years of hands-on experience in Software Engineering, Full Stack Development, Platform Engineering, Data Engineering, or related technical fields.
Minimum 3+ years of experience designing, developing, or deploying AI, Machine Learning, or Generative AI solutions.
Demonstrated success transitioning enterprise applications from traditional software architectures to AI-enabled solutions.
Proven experience building and deploying scalable production applications in cloud environments.
Experience integrating AI capabilities into business applications, workflows, and enterprise platforms.
Experience working with modern AI frameworks, LLMs, and agent-based application architectures.
Experience operating within Agile software development environments.
Experience collaborating with product managers, data scientists, architects, and business stakeholders to deliver innovative solutions.
Proven ability to lead technical initiatives and mentor engineering teams.
Nice to Have
Experience fine-tuning, evaluating, or optimizing open-source models.
Experience with Databricks Mosaic AI, MLflow, or other enterprise AI development platforms.
Knowledge of advanced AI techniques such as multi-agent systems, AI workflow orchestration, and autonomous agents.
Experience with graph databases such as Neo4j and knowledge graph implementations.
Familiarity with Deep Learning frameworks including PyTorch and TensorFlow.
Experience building enterprise Copilots and conversational AI solutions.
Azure AI Engineer Associate, Azure Developer Associate, Databricks, or equivalent cloud certifications.
Understanding of Responsible AI, AI governance, and enterprise compliance requirements.
Competencies
Artificial Intelligence & Generative AI
Software Engineering Excellence
Cloud & AI Platform Architecture
Problem Solving
Communicates Effectively
Competencies: Security & Compliance Principles - Applies standards, tools, and best practices to embed security, privacy, and compliance into the design/build/test/operate lifecycle for products, services, apps, systems, software, and configurations—balancing protection, efficiency, and cost.
Programming Principles - Applies programming languages, frameworks, and patterns to design, write, configure, test, and maintain software/solutions/systems that are efficient, secure, scalable, and reliable.
Data Principles - Governs, models, secures, implements, and observes data flows to ensure integrity, quality, and compliance—enabling trusted, scalable, cost-conscious data use.
Modern Development Practices - Applies modern engineering practices and tools—such as Agile/DevSecOps, CI/CD, automated testing, and infrastructure as code—to accelerate delivery, improv

Work arrangement
No