38:I[536569,["/_next/static/chunks/00ybbpx68_xw2.js","/_next/static/chunks/02jb5pnj98qe_.js","/_next/static/chunks/0i.l9589uvx0j.js","/_next/static/chunks/09xwjailhp6jd.js","/_next/static/chunks/0h7y588v_o8wu.js","/_next/static/chunks/03e85xkns9n81.js"],"default"] 28:["$","span","Deep Learning",{"className":"text-xs font-medium bg-[var(--color-brand-50)] text-[var(--color-brand-700)] border border-[#c7d2fe] px-2.5 py-1 rounded-[var(--radius-sm)]","children":"Deep Learning"}] 29:["$","span","Process Improvement",{"className":"text-xs font-medium bg-[var(--color-brand-50)] text-[var(--color-brand-700)] border border-[#c7d2fe] px-2.5 py-1 rounded-[var(--radius-sm)]","children":"Process Improvement"}] 2a:["$","span","Python",{"className":"text-xs font-medium bg-[var(--color-brand-50)] text-[var(--color-brand-700)] border border-[#c7d2fe] px-2.5 py-1 rounded-[var(--radius-sm)]","children":"Python"}] 2b:["$","span","Spark",{"className":"text-xs font-medium bg-[var(--color-brand-50)] text-[var(--color-brand-700)] border border-[#c7d2fe] px-2.5 py-1 rounded-[var(--radius-sm)]","children":"Spark"}] 2c:["$","span","Data Analysis",{"className":"text-xs font-medium bg-[var(--color-brand-50)] text-[var(--color-brand-700)] border border-[#c7d2fe] px-2.5 py-1 rounded-[var(--radius-sm)]","children":"Data Analysis"}] 2d:["$","span","Computer Vision",{"className":"text-xs font-medium bg-[var(--color-brand-50)] text-[var(--color-brand-700)] border border-[#c7d2fe] px-2.5 py-1 rounded-[var(--radius-sm)]","children":"Computer Vision"}] 2e:["$","span","NLP",{"className":"text-xs font-medium bg-[var(--color-brand-50)] text-[var(--color-brand-700)] border border-[#c7d2fe] px-2.5 py-1 rounded-[var(--radius-sm)]","children":"NLP"}] 2f:["$","span","TensorFlow",{"className":"text-xs font-medium bg-[var(--color-brand-50)] text-[var(--color-brand-700)] border border-[#c7d2fe] px-2.5 py-1 rounded-[var(--radius-sm)]","children":"TensorFlow"}] 30:["$","span","PyTorch",{"className":"text-xs font-medium bg-[var(--color-brand-50)] text-[var(--color-brand-700)] border border-[#c7d2fe] px-2.5 py-1 rounded-[var(--radius-sm)]","children":"PyTorch"}] 31:["$","span","Wfo",{"className":"text-xs font-medium bg-[var(--color-brand-50)] text-[var(--color-brand-700)] border border-[#c7d2fe] px-2.5 py-1 rounded-[var(--radius-sm)]","children":"Wfo"}] 32:["$","span","Aiml",{"className":"text-xs font-medium bg-[var(--color-brand-50)] text-[var(--color-brand-700)] border border-[#c7d2fe] px-2.5 py-1 rounded-[var(--radius-sm)]","children":"Aiml"}] 33:["$","span","Artificial Intelligence",{"className":"text-xs font-medium bg-[var(--color-brand-50)] text-[var(--color-brand-700)] border border-[#c7d2fe] px-2.5 py-1 rounded-[var(--radius-sm)]","children":"Artificial Intelligence"}] 34:["$","span","generative ai",{"className":"text-xs font-medium bg-[var(--color-brand-50)] text-[var(--color-brand-700)] border border-[#c7d2fe] px-2.5 py-1 rounded-[var(--radius-sm)]","children":"generative ai"}] 37:T407,Experience required: 5-10 Yrs Skills: Machine learning, Machine, Deep Learning, Wfo, Aiml, Data, Artificial Intelligence, generative ai

Roles and Responsibilities :

Design, develop, and deploy AI/ML models using deep learning techniques to solve complex business problems.
Collaborate with cross-functional teams to identify opportunities for process improvement and implement data-driven solutions.
Develop and maintain large-scale machine learning pipelines using Python, Spark, and other relevant tools.
Conduct exploratory data analysis, feature engineering, and model evaluation to drive business insights.

Job Requirements :

5.6-10 years of experience in AI/ML research or industry with a focus on deep learning.
Strong background in computer vision, natural language processing (NLP), or related fields.
Proficiency in programming languages such as Python and familiarity with popular libraries like TensorFlow or PyTorch.

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