Company Description About Nagarro In a changing and evolving world, challenges are ever more unique and complex. Nagarro helps to transform, adapt, and build new ways into the future through a forward-thinking, agile, and caring mindset. Today, we are 18,000+ experts across 37+ countries, forming a Nation of Nagarrians, ready to help our customers succeed. The nature of IT & digital product engineering has reached an incredible state of velocity and transition.
We must adapt and meet it with an agile mindset that isn't afraid to iterate towards the perfect solution. If we only solve today's problems, it's not enough. We must do more. We must courageously embrace the future, with vision and clarity about where technology & business are heading. Thinking breakthroughs gets us there. Nagarro - https://www.nagarro.com/en Job Description Must have Skills : Databricks, ML Engineering & MLOps ” Critical, Azure + Kubernetes ” Critical, GenAI / LLM Engineering  Job Purpose To design, prototype, and build next-generation analytic engines and services by applying strong expertise in Artificial Intelligence (AI) Purpose Build, deploy, scale and support machine learning, AI and GenAI solutions in production.
The role focuses on operationalising models, developing AI applications and agents, and creating the platforms and services required to deliver business value at scale. Key Responsibilities Productionise, deploy and monitor machine learning models and data science pipelines on Databricks. Build, deploy and support AI Agents, GenAI applications and RAG solutions on Databricks. Develop and maintain reusable ML pipelines using MLOps principles, including CI/CD, automated testing, monitoring and governance.
Deploy, optimise and manage open source AI and machine learning models on Azure Kubernetes Service (AKS). Design, develop and support custom APIs and microservices on AKS to expose AI and machine learning capabilities to business applications. Implement containerised solutions using Docker and Kubernetes to ensure scalable, secure and resilient deployments. Monitor model performance, drift, reliability and operational health in production environments.
Partner with Data Scientists to productionise prototypes and enable business-ready solutions. Collaborate with platform, security, cloud and infrastructure teams to ensure compliance with enterprise standards. Troubleshoot and resolve production issues related to models, pipelines, APIs and AI applications. Optimise AI and ML solutions for performance, scalability, cost and reliability. Contribute to engineering standards, reusable frameworks and best practices across the AI and ML ecosystem.
Mentor junior engineers and promote knowledge sharing across the team. Stay current with advancements in AI, GenAI, MLOps, Databricks, Kubernetes and cloud technologies. Core Deliverables Production-ready ML models and pipelines running on Databricks. AI Agents and business applications deployed on Databricks. Open source LLMs and AI services deployed on AKS. Secure and scalable APIs exposing AI capabilities to consuming systems.
Automated deployment, monitoring and governance processes. Reliable, scalable and compliant AI platforms supporting business outcomes. Key Skills Databricks Workflows, Model Serving, MLflow and Mosaic AI Azure Kubernetes Service (AKS) Python, SQL and REST APIs Docker and Kubernetes CI/CD and MLOps practices Machine Learning and Generative AI LLM deployment and optimisation Cloud engineering and infrastructure automation Monitoring, observability and troubleshooting Computer Science, Engineering, Econometrics, Mathematical Statistics, Actuary Science.
Masters or Doctorate will be an added advantage. Preferred Certifications Microsoft Azure certifications (AZ-104, AZ-305, AI-102 or equivalent) Databricks certifications (Data Engineer, Machine Learning Engineer, Generative AI Engineer) Kubernetes and containerisation certifications (CKA, CKAD or equivalent) De