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Strong Tier-1 employer and metro location increase applicant density, though senior specialized role limits broad competition.
Deep LLM, GPU, and ML engineering requirements make cross-industry transferability low.
Explicit 11-15 years plus mandatory GenAI/LLM and tooling requirements make filters highly stringent.
Design and manage RAG/LLM pipelines covering experiment, model, feature management, and model retraining.
Design scalable APIs for model inferencing leveraging frameworks like MLflow, SageMaker, Vertex AI, and Azure AI.
Lead GPU architecture optimization and deployment of large language models using DevOps and LLMOps tools such as Kubernetes, Docker, Flowise, Langflow, and Langgraph.
11-15 years of relevant experience in AI/ML engineering and architecture roles.
Mandatory technical skills: Gen AI, LLM, Hugging Face, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Mandatory education: Bachelor of Engineering, Master of Business Administration, or Master of Engineering.
Proficiency required in cloud platforms AWS, Azure, GCP and in DevOps tools like Kubernetes, Docker, container orchestration frameworks.
Experienced in advanced AI/ML pipeline architecture with strong hands-on expertise in large language models and GPU optimization.
Familiar with managing AI/ML model lifecycle through tools like MLflow, SageMaker, and open source LLM orchestration frameworks.
Operates effectively in advisory or consulting environment delivering scalable AI solutions to clients within large enterprises.