Job Description

About Company:Client is a global technology company, home to more than 224,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending December 2023 totaled $13.1 billion. To learn how we can supercharge progress for you,An Ideal Candidate:Experience in developing and implementing generative AI models, with a strong understanding of deep learning techniques such as GPT, VAE, and GANs. Model Evaluation & Optimization: Evaluate LLM\'s zero-shot and few-shot capabilities, fine-tuning hyperparameters, ensuring task generalization, and exploring model interpretability for robust web app integration.Key Competencies:
1. Proficient in Python, with experience in machine learning, deep learning, and NLP processing.
2. \xc2\xa7 Experience in developing and implementing generative AI models, with a strong understanding of deep learning techniques such as GPT, VAE, and GANs.
3. \xc2\xa7 Proficient in Langchain, LLM 4. \xc2\xa7 Prompt Engineering: The engineer prompts and optimizes few-shot techniques to enhance LLM\'s performance on specific tasks, e.g. personalized recommendations.
5. \xc2\xa7 Model Evaluation & Optimization: Evaluate LLM\'s zero-shot and few-shot capabilities, fine-tuning hyperparameters, ensuring task generalization, and exploring model interpretability for robust web app integration.
6. \xc2\xa7 Response Quality: Collaborate with ML and Integration engineers to leverage LLM\'s pre-trained potential, delivering contextually appropriate responses in a user-friendly web app.
7. \xc2\xa7 It is essential to have a solid understanding of data structures, algorithms, and principles of software engineering.
8. \xc2\xa7 Experience with vector databases RDBMS, MongoDB and NoSQL databases.
9. \xc2\xa7 Proficiency in working with embeddings.
10. \xc2\xa7 Strong distributed systems skills and system architecture skills 11. \xc2\xa7 Experienced in building and running a large platform at scale.
12. \xc2\xa7 Hands-on experience with Python, Hugging Face, TensorFlow, Keras, PyTorch, Spark, or similar statistical tools.
13. \xc2\xa7 Experience as data modeling ML/NLP scientist. including, but not limited to, Performance tuning, fine-tuning, RLHF, and performance optimization. Validated background with ML toolkits, such as PyTorch, TensorFlow, Keras, Langchain, Llamadindex, SparkML, or Databricks.
14. \xc2\xa7 Proficient with the integration of data from multiple data sources Experience with NoSQL databases, such as HBase, ElasticSearch, and MongoDB API Design. API/Data mapping to schema.
15. \xc2\xa7 Experienced in and strong knowledge of using AI/ML and more particularly LLMs eager to apply this rapidly changing technology.
16. \xc2\xa7 Good Knowledge of Kubernetes, and RESTful design.
17. \xc2\xa7 Prior experience in developing public cloud services or open-source ML software is an advantage

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