GenAI SME

Job Description SN Required Information Details 1 Role Gen AI SME 2 Required Technical Skill Set (Skill Name) Hands on in generative AI models (GPT, DALL-E, Stable Diffusion, Llama, BERT) Knowledge in frameworks (e.g., LangChain, Hugging Face Transformers) Knowledge in workflows (e.g., chatbots, document automation, creative tools) Hands on in cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI) and edge devices Knowledge in data privacy regulations (GDPR, CCPA) and ethical AI guidelines IBM Z(Mainframe) and IBM I (Midrange) Generative AI (LLMs, GANs, VAEs, etc.) Mandatory: Deep expertise in generative architecture (GPT, Transformers, Diffusion Models) Mandatory: Python, PyTorch/TensorFlow, and cloud platforms Multimodal AI (text-to-image, video synthesis) Reinforcement learning (RLHF) for model alignment AI ethics frameworks (e.g., AI Fairness 360) 3 No. of Requirements 2 4 Desired Experience Range 5 yrs to 15 yrs 5 Location of Requirement Hyderabad 6 Keywords GPT, DALL-E, Stable Diffusion, Llama, BERT., LangChain, Hugging Face Transformers, chatbots, document automation, creative tools AWS SageMaker, Azure ML, GCP Vertex AI, GDPR, CCPA, PyTorch, TensorFlow,Kubeflow, LLMs, GANs, VAEs. Desired Competencies (Technical/Behavioral Competency) Must-Have Minimum 5 mandate details are mandate with two or 3 liners Familiarity with Mainframe and Midrange platforms is essential for integrating Gen AI solutions into legacy enterprise systems. Candidate should understand data structures, interfaces, and operational workflows on platforms like IBM Z(Mainframe) and IBM I (Midrange). Experience in modernizing and bridging AI applications with these systems is a strong advantage. master s or PhD in Computer Science, Machine Learning, or related field (or equivalent experience) 5+ years in AI/ML development, with 3+ years focused on generative AI (LLMs, GANs, VAEs, etc.) Proven track record of deploying generative AI solutions in production environments mandatory technical skills: deep expertise in generative architectures (GPT, Transformers, Diffusion Models) mandatory proficiency in Python, PyTorch/TensorFlow, and cloud platforms experience with NLP tools (Hugging Face, spaCy) and vector databases (Pinecone, FAISS) preferred: knowledge of multimodal AI (text-to-image, video synthesis) experience with reinforcement learning (RLHF) for model alignment preferred familiarity with AI ethics frameworks (e.g., AI Fairness 360) Good-to-Have Minimum 2 mandate details are mandate with two or 3 liners strong analytical and problem-solving skills, excellent communication and collaboration abilities, attention to detail and ability to work independently SN Role descriptions / Expectations from the Role 1 We re looking for Gen AI SME to have the following Technical & Functional knowledge: architect, train, and fine-tune generative AI models (e.g., GPT, DALL-E, Stable Diffusion, Llama, BERT) for applications such as content generation, conversational AI, code automation, and data synthesis lead R&D initiatives to improve model performance, efficiency, and alignment with business goals evaluate and adapt emerging frameworks (e.g., LangChain, Hugging Face Transformers) partner with product, engineering, and business teams to integrate generative AI into workflows (e.g., chatbots, document automation, creative tools) translate business requirements into technical specifications for generative AI use cases optimize generative models for latency, cost, and scalability using techniques like quantization, distillation, and GPU/TPU acceleration deploy models on cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI) and edge devices implement safeguards to mitigate risks such as bias, misinformation, and harmful outputs ensure compliance with data privacy regulations (GDPR, CCPA) and ethical AI guidelines mentor junior engineers and data scientists on generative AI best practices publish research, contribute to patents, and represent the company at conferences/workshops build pipelines for data preprocessing, model training, and inference using tools like PyTorch, TensorFlow, and Kubeflow leverage MLOps practices for continuous integration, monitoring, and retraining Type Details of the Role (For Candidate Briefing) Reporting To Which Role Size of the Team, if any Reporting to this Role On-site Opportunity Unique Selling Proposition (USP) of The Role Details of The Project (A short Briefing on the Project can be provided herewith. It may be shared with external stakeholders like job-agencies etc. .