17 hours ago
Pune, IndiaSenior
Responsibilities
- Design, build, validate, deploy, and maintain production-grade AI applications, intelligent agents, and reusable enterprise AI capabilities.
- Develop LLM applications, generative AI solutions, agentic workflows, multi-agent systems, orchestration frameworks, and retrieval-augmented generation architectures.
- Build Python applications, APIs, microservices, and event-driven systems using scalable and secure software engineering practices.
- Integrate structured and unstructured enterprise data, knowledge repositories, vector databases, semantic search, and embedding-based retrieval.
- Design and operate AI platforms on Azure, including model serving, inference optimization, monitoring, MLOps, and LLMOps capabilities.
- Establish reusable AI frameworks, engineering standards, architecture patterns, governance practices, and responsible AI controls.
- Collaborate with product owners, data scientists, data engineers, architects, and business stakeholders to translate needs into scalable AI solutions.
- Lead technical initiatives, communicate architecture decisions, mentor engineering teams, and support modernization of traditional applications into AI-enabled systems.
Requirements
- College, university, or equivalent degree in Computer Science, Engineering, or a related field, or relevant equivalent experience.
- At least 8 years of hands-on experience in software engineering, full-stack development, platform engineering, data engineering, or related technical fields.
- At least 3 years of experience designing, developing, or deploying AI, machine learning, or generative AI solutions.
- Strong experience building, deploying, and maintaining AI-powered applications, intelligent agents, LLM applications, and generative AI solutions.
- Strong Python and production application development experience, including APIs, object-oriented design, design patterns, testing, secure coding, and scalable architectures.
- Experience with agentic AI workflows, multi-agent systems, AI orchestration, prompt engineering, evaluation, guardrails, grounding, observability, RAG, semantic search, and vector databases.
- Experience with Azure AI services, Azure OpenAI, Azure Databricks, Azure Functions, Azure Kubernetes Service, Azure storage and data services, Docker, and Kubernetes.
- Experience with data pipelines, feature engineering, data preparation, data lakes, warehouses, lakehouses, distributed systems, and enterprise data integration.
- Experience with cloud-native deployment, MLOps or LLMOps, model lifecycle management, monitoring, and CI/CD using Git, Azure DevOps, Jenkins, or equivalent tools.
- Experience designing enterprise-scale AI platforms, optimizing performance and reliability, evaluating technologies, and establishing reusable frameworks and standards.
- Experience collaborating with product managers, data scientists, architects, and business stakeholders, plus demonstrated technical leadership and mentoring ability.
- Preferred qualifications include model fine-tuning and evaluation, Databricks Mosaic AI, MLflow, graph databases such as Neo4j, knowledge graphs, PyTorch, TensorFlow, enterprise copilots, conversational AI, and relevant Azure or Databricks certifications.
Tech Stack
Categories
About Cummins
Cummins Inc. designs and manufactures diesel, natural gas, and electrified powertrains, generators, and related components (turbochargers, filtration, emission systems) for commercial vehicles, industrial equipment, marine, rail, and power markets. It sells to OEMs and fleet operators and provides parts and service through a global distribution network. Founded in 1919 and headquartered in Columbus, Indiana, Cummins is a public company listed on the NYSE (CMI).