Staff Engineer (Machine Learning, FastAPI, MLFlow)

We're Nagarro.

We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at a scale across all devices and digital mediums, and our people exist everywhere in the world (18000+ experts across 36 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!

Requirements

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
• Strong experience in Machine Learning model development, deployment, and production support.
• Hands-on expertise in Python with strong coding, debugging, and software engineering practices.
• Strong experience in building REST APIs and model-serving solutions using FastAPI.
• Experience with MLflow for experiment tracking, model registry, lifecycle management, and deployment workflows.
• Proven experience in designing and deploying machine learning solutions in production environments.
• Hands-on experience with Docker and Kubernetes for containerization and orchestration.
• Experience with workflow orchestration tools such as Airflow or Kubeflow.
• Knowledge of model-serving frameworks such as FastAPI, TorchServe, or Triton Inference Server.
• Experience with CI/CD tools such as GitHub Actions, GitLab CI, or ArgoCD.
• Strong understanding of MLOps concepts, model lifecycle management, monitoring, and governance.
• Experience with monitoring and observability tools such as Prometheus, Grafana, Evidently AI, or WhyLabs.
• Familiarity with experiment tracking and MLOps platforms such as MLflow, Weights & Biases, or DVC.
• Strong understanding of machine learning algorithms, model evaluation techniques, feature engineering, and data validation.
• Experience working with NLP, Large Language Models (LLMs), retrieval-augmented generation (RAG), or generative AI solutions is preferred.
• Strong analytical, problem-solving, communication, and stakeholder management skills.
• Ability to work in an Agile environment and collaborate effectively across cross-functional teams.

Responsibilities

• Own end-to-end machine learning model development and productionization, from problem framing and model development to deployment, monitoring, optimization, and scaling.
• Design, develop, train, validate, and deploy machine learning models for classification, regression, anomaly detection, forecasting, and NLP/LLM-based use cases.
• Build robust and scalable APIs using FastAPI for real-time model serving and integration with enterprise applications.
• Develop and maintain automated CI/CD pipelines for model training, testing, validation, deployment, and rollback processes.
• Containerize machine learning solutions using Docker and deploy them in Kubernetes-based environments.
• Implement batch and real-time inference solutions with high availability, performance, and scalability.
• Establish monitoring frameworks to track model performance, drift, latency, data quality, and operational health.
• Collaborate closely with business stakeholders, product teams, data scientists, and engineering teams to translate business requirements into production-ready ML solutions.
• Ensure reproducibility, version control, experiment tracking, and governance across the machine learning lifecycle.
• Define and manage model versioning, rollback strategies, A/B testing, and shadow deployment approaches.
• Optimize model serving infrastructure and deployment pipelines to improve efficiency, reliability, and cost-effectiveness.
• Contribute to MLOps best practices, automation frameworks, and continuous improvement initiatives.