Overview: 

Goldman Sachs Investment Banking (IB) works on some of the most complex financial challenges and transactions in the market today. Whether advising on a merger, providing financial solutions for an acquisition, or structuring an initial public offering, we handle projects that help clients at major milestones. We work with corporations, pension funds, financial sponsors, and governments and are team of strong analytical thinkers, who have a passion for producing out-of-the-box ideas.

Key Responsibilities: 

Architectural Patterns: In-depth knowledge of enterprise integration patterns, domain-driven design (DDD), and various architectural styles (e.g., monolithic, microservices, event-driven).

System Design: Ability to design highly scalable, available, resilient, and performant systems. This includes capacity planning, load balancing, caching strategies, and disaster recovery.

Data Management: Experience with relational databases (e.g., PostgreSQL, MySQL, Oracle) and NoSQL databases (e.g., MongoDB, Cassandra, Redis, DynamoDB). Understanding of data modelling, database optimization, and data migration strategies.

Messaging & Streaming: Experience with message brokers and streaming platforms like Apache Kafka, RabbitMQ, AWS SQS/SNS etc.

API Development: Proficiency in designing and developing RESTful APIs, GraphQL, and understanding API gateway concepts

Understanding of Cloud Providers: Proficiency in AWS (EC2, S3, RDS, Lambda, SQS, SNS, VPC, CloudFormation, EKS) is crucial, including their core services and architectural best practices.

Cloud Architecture Patterns: Experience with designing and implementing cloud-native architectures such as microservices, serverless, event-driven, and containerized applications.

Agentic AI Principles:  Understanding the concepts of intelligent agents, their architectures (e.g., perception-action cycles, memory, planning), and how they interact with environments. Knowledge of how LLMs work, prompt engineering, fine-tuning, and integrating them into applications. Concepts related to how AI agents make decisions, plan actions, and achieve goals. Understanding biases, fairness, transparency, and responsible AI development, especially critical for agentic systems.

Soft skills :

Technical Leadership: Ability to lead technical teams, mentor junior developers, and drive architectural decisions.

Communication: Excellent verbal and written communication skills to articulate complex technical concepts to both technical and non-technical stakeholders.

Problem-Solving: Strong analytical and problem-solving abilities to troubleshoot complex issues and design innovative solutions.

Stakeholder Management: Capability to collaborate effectively with product owners, project managers, and other teams to align technical solutions with business goals.