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Senior AI Engineer

Cummins Inc.
Pune, IND
Full-time
Senior · 8+ years experience
Onsite
Discovered 3 weeks ago
PythonAzureKubernetesDockerPyTorchTensorFlow

Full Job Posting

Qualifications

  • 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.
  • 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.
  • 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 (R
  • 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.
  • 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
  • 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 architecture
  • 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,
  • 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
  • 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 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.
  • 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.
  • 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.
  • Artificial Intelligence & Generative AI
  • Software Engineering Excellence
  • Cloud & AI Platform Architecture
  • Problem Solving
  • Communicates Effectively

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

Responsibilities

  • 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

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