4 days ago
Pune, IndiaSenior
Responsibilities
- Design, build, validate, deploy, and maintain production-grade AI-enabled applications, intelligent agents, and reusable AI services.
- Develop LLM applications, agentic workflows, multi-agent systems, AI orchestration, prompt engineering, evaluation, guardrails, grounding, and observability capabilities.
- Build software applications, REST APIs, microservices, and event-driven architectures using secure and scalable engineering practices.
- Integrate structured and unstructured enterprise data, data pipelines, vector databases, semantic search, knowledge repositories, and embedding-based retrieval.
- Design enterprise-scale AI platforms and architectures aligned with business objectives, performance, scalability, reliability, and operational requirements.
- Deploy and operate AI workloads using Azure services, Docker, Kubernetes, MLOps, LLMOps, monitoring, and CI/CD pipelines.
- Establish engineering standards, reusable frameworks, governance, security, responsible AI, compliance, and best practices for AI solution delivery.
- Collaborate with product owners, data scientists, data engineers, architects, business stakeholders, users, and technical teams to gather requirements and deliver solutions.
- Lead technical initiatives, mentor engineering teams, communicate architecture decisions, and support continuous improvement across the software development lifecycle.
Requirements
- A college, university, or equivalent degree in Computer Science, Engineering, or a related subject, or relevant equivalent experience is required.
- 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, production applications, and cloud solutions.
- Expertise in LLM application development, generative AI solution design, agent architectures, AI orchestration, RAG, semantic search, vector databases, and knowledge management architectures.
- Strong Python and software engineering experience, including object-oriented design, design patterns, testing, secure coding, APIs, microservices, distributed systems, and scalable application architecture.
- Experience with Azure AI services, Azure OpenAI, Azure Databricks, Spark, Azure Functions, Azure Kubernetes Service, Azure Storage, and related data services.
- Experience with Docker, Kubernetes, MLOps, LLMOps, model lifecycle management, monitoring, cloud deployment, and CI/CD platforms such as Git, Azure DevOps, or Jenkins.
- Experience designing enterprise AI platforms, evaluating AI technologies, optimizing model serving and inference, and establishing reusable engineering frameworks and standards.
- Strong analytical, problem-solving, communication, technical leadership, mentoring, decision-making, and customer-focused skills.
- Preferred experience includes fine-tuning or evaluating open-source models, Databricks Mosaic AI, MLflow, Neo4j, knowledge graphs, PyTorch, TensorFlow, enterprise copilots, conversational AI, and 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).