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Generalist ML role, 2–5 yrs, metro Hyderabad, broad MLOps/GenAI skills, high applicant competition.
Specialized ML and MLOps requirements make cross-industry transitions moderately difficult but still somewhat transferable.
Explicit 2–5 years plus mandatory MLOps, cloud, Docker/Kubernetes and production requirements increase strictness.
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Develop and deploy end-to-end ML pipelines and production-grade AI/GenAI applications using modern MLOps platforms such as Kubeflow and SageMaker.
Convert research and prototype ML/AI models into scalable, secure, and maintainable services, leveraging Docker, Kubernetes, and cloud infrastructure.
Implement monitoring, observability, security, and responsible-AI practices for production ML applications to ensure performance, reliability, and compliance.
2–5 years of experience in machine learning, AI, data engineering or enterprise software development.
Strong proficiency in Python programming and production software development, with experience in building REST APIs and microservices.
Hands-on experience with containerization (Docker) and orchestration (Kubernetes) technologies and major cloud platforms such as AWS including managed AI/ML services.
Working knowledge of ML frameworks and MLOps tools such as Kubeflow, SageMaker Pipelines, MLflow, or equivalent; understanding of model monitoring, drift detection, and responsible AI concepts.
Experienced in operationalizing ML models from prototype to production in cloud environments with strong software engineering skills.
Familiar with building AI/GenAI applications integrating LLMs, prompt engineering, vector databases, and retrieval-augmented generation.
Collaborative and effective communicator who can translate business requirements into reliable ML/AI solutions balancing performance, scalability, and cost.