Data Scientist (GenAI)

Job Description -
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As a Consultant and with service delivery as your primary focus, you will be responsible for providing both business and technical services for a range of customer -facing consulting activities that specialize in Generative AI. Your role will involve aiding customers as they plan and strategize their Generative AI initiatives. Your technical proficiency and customer -facing knowledge, in addition to strong and trustworthy partnership, is necessary to cultivate customer relationships and ensure clarity of project objectives. The necessary requirements for this position are listed below
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Requirement and skills
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Candidate should have 5+ years of experience in the fields of Artificial Intelligence and Big Data with 3+ years of progressive professional growth in a customer -facing services delivery role.
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· Must have the ability to lead discussions with C -level customer roles, drive consensus, create strategic roadmaps, and engage data scientists on exploring existing and future ecosystems.
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· Possess deep knowledge of Generative AI, particularly with model selection, data preparation for GenAI training, model finetuning and customization, and guardrail implementation; understanding of the GenAI tooling ecosystem including Kubernetes, MLops, and AIops tools.
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· Ability to gain customer trust, plan, organize, and drive customer workshops, and present findings.
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· Demonstrated capacity to articulate complex concepts, build consensus and deliver outstanding work products.
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· Proven experience in conducting architectural analyses, benefits analyses, and discoveries related to IT engagements.
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· Must have prior experience working directly with clients, providing engaging presentations and accurate documentation while leading workshops and interviews.
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· The ideal candidate should be able to deliver tailored technology services on an enterprise -wide level while adhering to margin, planning and SOW requirements.
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· On -site customer workshops may be required.
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· Familiarity with Nvidia AI tools, including Bright Command Manager, Nemo Framework, and Triton Inference Server
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· Proficiency in Large Language Models (LLM)
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· Competency using MLOps and AIOps tools such as cnvrg.io, mlfow, and Domino
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· Expertise in Lean and Iterative Deployment Methodologies
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· Educational Background in Machine Learning is an asset
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· Certifications in relevant fields is preferred
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