Compliance Engineering - Neon Platform - AI/ML Engineer - Associate - Hyder at Goldman Sachs

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

02 Jun, 26

Salary

0.0

Posted On

04 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI/ML, Large Language Models, GenAI, Agentic Frameworks, RAG Pipelines, Model Fine-tuning, Prompt Engineering, Python, TensorFlow, PyTorch, Hugging Face Transformers, Scikit-learn, Embedding Models, Vector Databases, MLOps, Docker

Industry

Financial Services

Description

YOUR IMPACT Are you passionate about leveraging cutting-edge AI/ML techniques, including Large Language Models, to solve complex, mission-critical problems in a dynamic environment? Do you want to contribute to safeguarding a leading global financial institution? OUR IMPACT We are Compliance Engineering, a global team of engineers and scientists dedicated to preventing, detecting, and mitigating regulatory and reputational risks across Goldman Sachs. We build and operate a suite of platforms and applications that protect the firm and its clients. We offer: * Access to petabyte scale of structured and unstructured data to fuel your AI/ML models, including textual data suitable for LLM applications. * The opportunity to work with state-of-the-art LLM models and agentic framework. * A collaborative environment where you can learn from and contribute to a team of experienced engineers and scientists. * The chance to make a tangible impact on the firm's ability to manage risk and maintain its reputation. Within Compliance Engineering, we are seeking an experienced AI/ML Engineer to join our Engineering team. This role will focus on solving highly complex business problems using AI/ML techniques, incorporating latest emerging trends om building out vertical AI agents to run on data at massive scale. HOW YOU WILL FULFILL YOUR POTENTIAL As a member of our team, you will: * Design, develop, and implement GenAI-driven solutions, including agentic frameworks for automating compliance processes, RAG pipelines, and embeddings of compliance knowledge bases. * Explore diverse AI/ML problems, such as model fine-tuning, prompt engineering, and experimentation with different algorithmic approaches to address novel business challenges. * Develop, test, and maintain high-quality, production-ready code. * Collaborate effectively with compliance officers, legal counsel, and other stakeholders to understand business requirements and translate them into technical solutions. * Participate in code reviews to ensure code quality, maintainability, and adherence to coding standards. Promote best practices for AI/ML development, including version control, testing, and documentation. * Stay current with the latest advancements in AI/ML platforms, tools, and techniques to solve business problems. QUALIFICATIONS A successful candidate will possess the following attributes: * A Bachelor's, master’s or PhD degree in Computer Science, Machine Learning, Mathematics, or a similar field of study. * Preferably 3+ years AI/ML industry experience for Bachelor’s/Masters, 1 year for PhD with a focus on Language Models. * Strong foundation in machine learning algorithms, including deep learning architectures (e.g., transformers, RNNs, CNNs) * Proficiency in Python and relevant libraries/frameworks such as TensorFlow, PyTorch, Hugging Face Transformers, scikit-learn. * Demonstrated expertise in GenAI techniques, including but not limited to Retrieval-Augmented Generation (RAG), model fine-tuning, prompt engineering, AI agents, and evaluation techniques. * Experience working with embedding models and vector databases. * Experience with MLOps practices, including model deployment, containerization (Docker, kubernetes), CI/CD, and model monitoring. * Strong verbal and written communication skills. * Curiosity, ownership and willingness to work in a collaborative environment. Experience in some of the following is desired and can set you apart from other candidates: * Experience with Agentic Frameworks (e.g., Langchain, AutoGen) and their application to real-world problems. * Understanding of scalability and performance optimization techniques for real-time inference such as quantization, pruning, and knowledge distillation. * Experience with model interpretability techniques. * Prior experience in code reviews/ architecture design for distributed systems. * Experience with data governance and data quality principles. * Familiarity with financial regulations and compliance requirements. ABOUT GOLDMAN SACHS At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers. We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html © The Goldman Sachs Group, Inc., 2026. All rights reserved. Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.

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Responsibilities

The role involves designing, developing, and implementing Generative AI-driven solutions, including agentic frameworks, RAG pipelines, and knowledge base embeddings for automating compliance processes. Responsibilities also include exploring diverse AI/ML problems like model fine-tuning and prompt engineering, while developing high-quality, production-ready code.