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Data Scientist (Python, ML, SQL) in Noida, India is listed on Jobeax. Browse 30,000+ vacancies available.

Model Development: Research, design, and develop state-of-the-art generative models such as GPT, GANs, VAEs, or diffusion models for tasks like text generation, summarization, reasoning, Q&A, or predictive analytics.

Data Preparation: Clean, preprocess, and structure large datasets to train, validate, and test generative AI models.
Deployment: Implement scalable solutions for deploying generative AI models in production environments using tools like Docker, Kubernetes, or cloud platforms (AWS, GCP, Azure).
Collaboration : Work closely with cross-functional teams, including product managers, engineers, and stakeholders, to align AI solutions with business goals.
Evaluation: Develop metrics and benchmarks to evaluate model performance and ensure quality outputs.
Research: Stay up to date with the latest advancements in AI/ML, particularly in generative AI techniques, and propose innovative solutions.
Ethical AI Practices: Experience of architecting AI systems to solve complex business problems.
Build advanced RAG pipelines, text chunking, and retrieval, LLM Prompt Engineering, using Vector Databases. Implement right LLM selection based on use cases and client criteria (GPT-4, Llama2, Mistral, Claude, Gemini, Flan, BERT) while managing trifecta of accuracy, cost, and latency/scale.
Develop, fine-tune, context tune and implement state-of-the-art NLP models including Large Language Models like GPT, https://jobeax.com/link/10NB10tyPdNbii5N AI systems & architectures, while considering good Responsible AI standards and AI Governance.
Build Agentic AI, AutoGen, Muti-agents use cases, AI Autonomous Agents and LLM orchestration architectures for enterprise data at scale in Python.
Hands on experience with complementary technologies around LLMs like embedders, vector databases (chroma, weaviate etc.), Collaborate with cross-functional teams to integrate generative AI solutions into real-world applications.
Stay up-to-date with the latest advancements in deep learning and generative models and apply them to enhance our AI capabilities.
Document research findings, prepare technical reports, and contribute to whitepaper/scientific publications.
Provide deep leadership and coaching in the project delivery lifecycle. Qualifications Education and Experience:
Masters of Science or PhD in computer science, data science, statistics, Natural Language Processing
2-3 years of experience in GenAI and Large Language Models.
Strong programming skills in Python, including experience with libraries like TensorFlow, PyTorch, Hugging Face Transformers, or similar. Solid understanding of optimization techniques for training deep neural networks, regularization methods, and hyperparameter/fine tuning.
Hands-on experience with generative AI models, such as Llama3, GPT4, Claude
Knowledge of NLP, computer vision, or multimodal AI techniques.
Proficiency in data manipulation and analysis tools (e.g., Pandas, NumPy, SQL).
Experience in Generative AI Models and LLMs, finetuning LLMs, prompt engineering and experience with LLM orchestration frameworks like Langchain, LlamaIndex, RAGAS, etc.
Strong software engineering skills for rapid and accurate development of AI models and systems.
Understanding of Agentic AI, Autonomous Agents, AI Agents, AutoGen, https://jobeax.com/link/x5OJTgUk5BytQUwi, Langchain, and workflow steps design. Google AI Agents.
Provide business-oriented solution with ability to communicate effectively, both verbally and in writing, with technical and non-technical stakeholders.
Experience working in a collaborative environment, contributing to multidisciplinary teams and projects.
Experience in deploying ML models in cloud environments (AWS SageMaker, GCP AI Platform, or Azure ML).
Excellent communication skills, both technical and non-technical.

5 years of experience in AI, NLP including transformer architecture and LLMs, Computer Vision and related technologies

Ability to explain GenAI to non-technical audiences across many different industries. Candidate is also hands on developer while people managing Data Scientists, ML Engineers etc.
Experience in ML Engineering and MLOps, MLFlow
Strong understanding of statistical and machine learning concepts
Experience with deep learning frameworks such as TensorFlow and PyTorch
Familiarity with key concepts and techniques used in generative models, such as variational autoencoders (VAEs), generative adversarial networks (GANs), and flow-based models.
Strong programming skills in languages such as Python, along with experience working with popular deep learning frameworks like PyTorch and TensorFlow.
Understanding of Graph Database and/or Vector Database along with knowledge of cloud services (e.g., AWS, Azure, GCP).
Experience with deploying AI models in production environments.
Familiarity with domain-specific applications of generative AI
Leveraged both Azure and AWS for model inferencing. For finetuning, he has worked more on AWS SageMaker.

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