Gen AI LLM Lead

3 weeks ago

New York, United States HCLTech Full time

GenAI Lead

Location : New York, New Jersey – Client location

Key Skill set: Data Science, GenAI, LLM, Vertex AI, Python


Roles and Responsibilities:


Educational Qualifications: Graduate or Doctorate degree in information technology, Banking domain experience required, Computer Science, Artificial Intelligence, or a related field. Specialization in Natural Language Processing is preferred.

Experience Requirements: 8-10 years of experience in developing Data Science, AI, and ML solutions, with a specific focus on generative AI and LLMs in the Finance/Banking domain.

Prior experience in identifying new opportunities to optimize the business through analytics, AI/ML and use case prioritization.

The individual should be a thought leader having a well-balanced analytical business acumen, domain, and technical expertise.

Large Language Model Expertise: Experience in working with and fine-tuning Large Language Models (LLMs), including the design, optimization of NLP systems, frameworks, and tools.

Application Development with LLMs: Experience in building scalable applications using LLMs, utilizing frameworks such as LangChain, LlamaIndex, etc and productionizing machine learning and AI models.

Skills required for Vertex AI, Knowledge of GCP platform is must.

Language Model Development: Utilize off-the-shelf LLM services, such as Azure OpenAI, Vertex AI, to integrate LLM capabilities into applications.

Cloud Computing Expertise: Proven architect kind of experience in cloud computing, particularly with Azure Cloud Services.

Technical Proficiency: Strong skills in UNIX/Linux environments and command-line tools.

Programming and ML Skills: Proficiency in Python, with a deep understanding of machine learning algorithms, deep learning, and generative models.

Advanced AI Skills and Testing: Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch), hands-on experience in deploying AI/ML solutions as a service/REST API on Cloud or Kubernetes, and proficiency in testing of developed AI components.

Responsibilities also include data analysis/preprocessing for training and fine-tuning language models. Also, solves virtually all issues around privacy, real-time, sparce data collection, passive data collection and security and regulatory requirements.

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