Machine Learning Engineer

Overview

Machine Learning Engineer role focused on building, deploying, and operating scalable machine-learning and generative-AI solutions.

What you'll do

• Develop end-to-end ML pipelines from data ingestion through deployment using Kubeflow, SageMaker Pipelines, or equivalent MLOps tools.
• Productionize ML and GenAI models by converting prototype code into reliable services packaged with Docker and Kubernetes.
• Expose model services through secure REST, gRPC, or event-driven APIs.
• Build GenAI applications using LLMs, prompt engineering, embeddings, vector databases, RAG, and agent-based frameworks.
• Implement monitoring and observability for production ML, including metrics, logging, tracing, drift detection, and dashboards.
• Apply security and responsible-AI practices including access controls, encryption, lineage, explainability, bias monitoring, and secure data handling.

What you'll need

• 2–5 years of experience in machine learning/AI, data engineering, or enterprise software development.
• Strong understanding of core ML algorithms such as regression, classification, tree-based ensembles, clustering, dimensionality reduction, and time-series.
• Experience with GenAI/agent frameworks such as LangChain or equivalents.
• Strong Python programming skills for production-quality software.
• Hands-on experience with Docker and Kubernetes or equivalent containerization/orchestration.
• Experience with a major cloud platform such as AWS and managed AI/ML services.

Details

• Location: Hyderabad, India.