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Tier-1 brand, mid-level GenAI role, and broad sought-after skillset increase applicant competition.
Core ML/LLM skills are transferable, but healthcare governance and domain specifics increase sensitivity to background.
Mandatory 4+ years plus many required ML/LLM, cloud, and infrastructure skills make shortlisting strict.
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Develop and implement NLP, LLM, and Generative AI models including data ingestion, preprocessing, and Retrieval Augmented Generation (RAG) for AI/ML pipelines.
Collaborate with MLOps and product teams to deploy, monitor, and optimize AI models ensuring technical and business requirements and SLAs are met.
Create strategy and roadmap for Gen AI model development lifecycle and apply best practices, governance, and performance evaluation methodologies.
Bachelor’s degree in Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, or equivalent.
Minimum 4 years of professional experience in AI/ML with hands-on use of Python, SQL, Hugging Face, TensorFlow, Keras, PyTorch, Spark, and cloud platforms (GCP/AWS).
Must be located within reasonable commuting distance to office; hybrid work requires 1-2 days onsite per week.
No visa sponsorship available; adherence to vaccination policies required (COVID-19 and Influenza).
Experienced in large-scale AI projects involving NLP, LLM fine-tuning, Generative AI, and infrastructure including deployment on cloud and containerized environments.
Has led Agile or Scaled Agile Frameworks (SAFe) projects with strong cross-functional collaboration involving MLOps, machine learning engineers, and business stakeholders.
Demonstrates advanced knowledge in LLM infrastructure and deployment optimizations such as model quantization, GPU memory optimization, and RAG architecture design.