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Mid-level GenAI role in a metro at a large services firm increases candidate competition.
Specialized GenAI, MLOps, and cloud AI skills limit cross-industry transferability.
Many mandatory GenAI, MLOps, cloud, and Python requirements make shortlisting highly restrictive.
Design, develop, and deploy Python-based Generative AI and ML applications integrating with cloud-native AI services (GCP/AWS/Azure).
Build scalable AI solutions including APIs, microservices, and MLOps pipelines with focus on performance, security, and cost optimization.
Collaborate cross-functionally with data scientists and product teams; contribute to knowledge repository via whitepapers and market research for AI use cases.
5-8 years of professional experience.
Strong hands-on Python development skills for Generative AI workloads.
Practical experience with Generative AI models (LLMs, transformers, diffusion models) and cloud-native AI services on GCP, AWS, or Azure.
Knowledge of privacy-preserving ML techniques, responsible AI practices, prompt engineering, and MLOps including deployment and monitoring.
Experienced in designing scalable, production-ready AI solutions with expertise in modern AI frameworks and cloud integration.
Able to work effectively across roles including data scientists, ML engineers, and product teams to deliver end-to-end AI solutions.
Contributes strategically by creating knowledge assets and identifying new AI use cases for business enhancement within consulting environments.