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In-demand GenAI role, mid-level range, metro location and broad cloud/LLM requirements increase competition.
Core ML/LLM skills transfer across industries, but enterprise GCP and deployment experience add moderate specificity.
Explicit 4–10 years plus mandatory Python, PyTorch/TensorFlow, GCP, and LLM deployment experience raises strictness.
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Design, develop, fine-tune, and deploy Large Language Models (LLMs) and Generative AI solutions within enterprise environments.
Build and implement scalable AI model pipelines, APIs, and microservices, optimizing for performance, scalability, latency, and cost.
Collaborate cross-functionally and provide technical leadership and mentorship on AI projects, ensuring security, compliance, and data privacy adherence.
4 to 10 years of software development experience with a focus on AI/ML technologies.
Proficient in Python (3.8+) and AI frameworks such as PyTorch or TensorFlow for model development.
Experience with Google Cloud Platform (GCP) services like BigQuery and Vertex AI or equivalent cloud AI platforms.
Bachelor’s or Master’s degree in Computer Science, IT, AI/ML, Data Science, or related field (or equivalent hands-on experience).
Experienced in developing and deploying Generative AI and Retrieval-Augmented Generation (RAG) architectures at scale in enterprise settings.
Strong background in building reusable ML pipelines, feature engineering frameworks, and integrating AI models via APIs/microservices.
Comfortable operating in cross-functional teams involving data engineering, product, cloud, and applications with leadership and mentorship responsibilities.