Goldman Sachs is Hiring ! 2022,2023 & 2024
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About Goldman Sachs
At Goldman Sachs, goldman sachs commit their people, capital and ideas to help their clients, shareholders and the communities they serve to grow. Founded in 1869, they are a leading global investment banking, securities and investment management firm. Headquartered in New York, they maintain offices around the world.
They believe who you are makes you better at what you do. They’re committed to fostering and advancing diversity and inclusion in their own workplace and beyond by ensuring every individual within their firm has a number of opportunities to grow professionally and personally, from their training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs.
Get more information about their culture, benefits, and people at GS.com/careers.
Job Role
NLP Software Engineer- Associate
Goldman Sachs are seeking a talented and experienced Senior AI Engineer to join their team and lead efforts in the design and implementation of generative AI solutions. They are specifically looking for someone with hands-on expertise in natural language processing (NLP) and Large Language Models (LLMs) and a passion to solve hard problems on unique financial datasets. This role will focus on implementing end-to-end solutions in the areas of search and information retrieval (IR); question answering and conversational AI; document summarization; entity, event, and relationship extraction and classification; as well as structured text extraction from documents.
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Eligibility Criteria
Batch : 2022, 2023 & 2024
Stream : Undergraduate/graduate degree in Computer Science or related fields.
- Proven experience in hands-on machine learning development, in a financial firm or a similar industry.
- Strong expertise in AI models and machine learning techniques, particularly in latest language models
- Experience with language modeling, prompt tuning and engineering, instruction tuning, and/or RLHF
- Experience with AI platforms and frameworks, such as TensorFlow, PyTorch, Keras, HuggingFace
- Experience with search and retrieval algorithms, conversational AI, and large-scale document AI techniques
- Excellent communication and collaboration skills to work effectively in a cross-functional team
- Demonstrated experience in driving business-critical projects using agile methodologies and best practices in software engineering
- Experience in Docker Containerization, K8s, API gateway, Software Load Balancer, gitlab CI/CD Pipeline, Observability tools like Prometheus. Build and enhance orchestration tools to enable semantic search data ingestion pipelines that will be used in document ingestion, vector embeddings for document digitization, search and retrieval and conversational services.
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Core Responsibilities
- Search and Retrieval:
- Utilize your expertise in language models to improve search and retrieval algorithms.
- Conduct thorough experiments and evaluate different AI models for various use cases.
- Select and implement the most suitable AI models for improving search and retrieval performance
- Conversational AI:
- Develop conversational AI services using large language models to enable interactions such as question answering and summarization
- Leverage open source language models (LLMs) and/or proprietary models and vendor platforms like Google/Microsoft/OpenAI to effectively solve customer problems
- Collaborate with cross-functional teams, including UX/UI designers, to deliver exceptional user experiences to stakeholders
- Model Development and Training:
- Build and train working versions of AI models using the GS NLP platform
- Leverage your knowledge of machine learning techniques to optimize and enhance the performance of the model
- Collaborate with the data engineering team to ensure efficient data pipelines for model training and push innovative deep learning models to production
- Reusable Patterns, Libraries, and Datasets:
- Identify and create reusable patterns, model catalogs, and datasets to improve efficiency and accelerate development
- Contribute to the company’s internal knowledge base by documenting best practices and sharing insights with the team
