Goldman Sachs 2025 Hiring Analyst-Software Engineering | INR 15-25 LPA

Qualifications, Experience, and Attributes:

• Bachelor's degree or higher in a quantitative subject such as Computer Science, Mathematics, Statistics, Engineering, or a related field.
• Proven commercial experience in designing, developing, and implementing AI and GenAI solutions, with a strong focus on practical applications and real-world impact.
• Demonstrated expertise in GenAI techniques, including but not limited to Retrieval-Augmented Generation (RAG), AI agents, and other advanced AI methodologies.
• Strong analytical and problem-solving skills, with the ability to translate complex business requirements into effective AI solutions.
• Excellent collaboration and communication skills, with the ability to work effectively with both technical and non-technical stakeholders.
• A proactive and innovative mindset, with a passion for staying at the forefront of AI and GenAI advancements and applying them to solve business challenges.
• Strong programming skills in languages commonly used in AI development, most importantly Python.
• Familiarity with AI and machine learning frameworks and tools, such as TensorFlow, PyTorch, or similar, is desirable.

Key Responsibilities:

• Design, develop, and implement advanced AI and GenAI solutions to address complex challenges in Risk, Controllers, Legal, and other domains.
• Collaborate with stakeholders from various divisions to identify and prioritize AI applications that can drive operational efficiency and process improvements.
• Leverage state-of-the-art GenAI techniques, including Retrieval-Augmented Generation (RAG), AI agents, and other advanced methodologies, to develop robust AI models and solutions.
• Partner with business leaders and classic business teams to understand their needs and integrate AI solutions seamlessly into their workflows.
• Work closely with Goldman Sachs Engineering teams to leverage their technical solutions and infrastructure, ensuring the successful deployment and scalability of AI applications.
• Continuously monitor and evaluate the performance of AI models, making necessary adjustments to optimize their effectiveness and accuracy.
• Stay abreast of the latest advancements in AI and GenAI technologies, and proactively identify opportunities to incorporate these innovations into our AI strategies.