Compliance Engineering - FCC Eng - Associate - Bengaluru
Goldman Sachs · Bengaluru, Karnataka, India · 2+ yrs experience · Posted 2026-07-01
Tech stack: Express, GraphQL, Java, JavaScript, Kafka, Kubernetes, React, TypeScript
About the role
Are you passionate about developing mission-critical, high quality software solutions, using cutting-edge technology, in a dynamic environment?
We are Compliance Engineering, a global team of more than 300 engineers and scientists who work on the most complex, mission-critical problems.
We:
build and operate a suite of platforms and applications that prevent, detect, and mitigate regulatory and reputational risk across the firm.
have access to the latest technology and to massive amounts of structured and unstructured data.
leverage modern frameworks to build responsive and intuitive front end and Big Data applications.
Responsibilities:
- As a member of our team, you will:
- partner globally with sponsors, users and engineering colleagues across multiple divisions to create end-to-end solutions,
- learn from experts
- leverage various technologies depending on the team including Java, JavaScript, TypeScript, React, APIs, GraphQL, Elastic Search, Kafka, Kubernetes
- be able to innovate and incubate new ideas
- have an opportunity to work on a broad range of problems, often dealing with large data sets, including real-time processing, messaging, workflow and UI/UX
- be involved in the full life cycle; defining, designing, implementing, testing, deploying, and maintaining software across our products.
- AI‑Assisted Engineering & Productivity
- In addition, you will:
- Effectively use
- AI‑assisted software development tools
- (e.g., GitHub Copilot, Devin, Claude Code, or equivalent) to improve developer productivity and reduce development cycle time.
- Apply AI tools to accelerate:
- code generation and refactoring,
- test creation and coverage improvement,
- debugging, root‑cause analysis, and performance optimization.
- Use AI‑assisted reasoning to understand complex codebases, rapidly prototype solutions, and improve code quality while maintaining strong engineering standards.
- Partner with peers and reviewers to
- validate, harden, and productionize AI‑generated outputs
- , ensuring correctness, security, maintainability, and regulatory compliance.
- Identify opportunities where AI tooling can reduce manual effort, minimize rework, and support faster, higher‑quality delivery to production.
Qualifications:
- A successful candidate will possess the following attributes:
- A Bachelor's or Master's degree in Computer Science, Computer Engineering, or a similar field of study.
- 2.5+ years professional software development experience
- Expertise in Java development.
- Experience in automated testing and SDLC concepts.
- The ability (and tenacity) to clearly express ideas and arguments in meetings and on paper.
- Experience in some of the following is desired and can set you apart from other candidates:
- Demonstrated experience or a strong interest in leveraging
- AI-assisted developer tools
- to enhance engineering productivity and accelerate software delivery.
- Ability to critically evaluate
- AI‑generated outputs
- and enhance them to production‑grade quality.
- Proven ability to critically evaluate
- AI-generated outputs
- and refine them to meet production-grade standards of quality, reliability, and maintainability.
- Hands-on proficiency with AI coding assistants such as
- GitHub Copilot
- Claude Code
- to expedite code authoring, refactoring, and debugging across all phases of the Software Development Life Cycle
- Effective utilization of AI pair-programming tools to generate boilerplate code, unit tests, and technical documentation, enabling greater focus on architectural design and complex problem-solving.
- Application of established
- prompt engineering
- best practices to elicit high-quality, context-aware code recommendations and reduce iteration cycles during development.
- Practical integration of autonomous
- AI agents (e.g., Devin)
- for routine engineering tasks, including bug triage, dependency upgrades, and minor feature implementations, thereby freeing engineering bandwidth for higher-impact initiatives.
- Adoption
- of AI-driven code review
- tools to proactively identify defects, security vulnerabilities, and performance bottlenecks earlier in the development cycle, strengthening overall quality gates.
- Experience with:
- UI/UX development
- API design, such as to create interconnected services
- message buses or real time processing
- relational databases
- knowledge of the financial industry and compliance or risk functions
- influencing and collaborating with stakeholders.
Qualifications
- A successful candidate will possess the following attributes:
- A Bachelor's or Master's degree in Computer Science, Computer Engineering, or a similar field of study.
- 2.5+ years professional software development experience
- Expertise in Java development.
- Experience in automated testing and SDLC concepts.
- The ability (and tenacity) to clearly express ideas and arguments in meetings and on paper.
- Experience in some of the following is desired and can set you apart from other candidates:
- Demonstrated experience or a strong interest in leveraging
- AI-assisted developer tools to enhance engineering productivity and accelerate software delivery.
- Ability to critically evaluate AI‑generated outputs and enhance them to production‑grade quality.
- Proven ability to critically evaluate AI-generated outputs and refine them to meet production-grade standards of quality, reliability, and maintainability.
- Hands-on proficiency with AI coding assistants such as GitHub Copilot Claude Code to expedite code authoring, refactoring, and debugging across all phases of the Software Development Life Cycle
- Effective utilization of AI pair-programming tools to generate boilerplate code, unit tests, and technical documentation, enabling greater focus on architectural design and complex problem-solving.
- Application of established prompt engineering best practices to elicit high-quality, context-aware code recommendations and reduce iteration cycles during development.
- Practical integration of autonomous
- AI agents (e.g., Devin)
- for routine engineering tasks, including bug triage, dependency upgrades, and minor feature implementations, thereby freeing engineering bandwidth for higher-impact initiatives.
- Adoption of AI-driven code review tools to proactively identify defects, security vulnerabilities, and performance bottlenecks earlier in the development cycle, strengthening overall quality gates.
- Experience with: UI/UX development
- API design, such as to create interconnected services message buses or real time processing relational databases knowledge of the financial industry and compliance or risk functions influencing and collaborating with stakeholders.
Responsibilities
- partner globally with sponsors, users and engineering colleagues across multiple divisions to create end-to-end solutions
- learn from experts
- leverage various technologies depending on the team including Java, JavaScript, TypeScript, React, APIs, GraphQL, Elastic Search, Kafka, Kubernetes
- be able to innovate and incubate new ideas
- have an opportunity to work on a broad range of problems, often dealing with large data sets, including real-time processing, messaging, workflow and UI/UX
- be involved in the full life cycle; defining, designing, implementing, testing, deploying, and maintaining software across our products.
- AI‑Assisted Engineering & Productivity In addition, you will: Effectively use
- AI‑assisted software development tools
- (e.g., GitHub Copilot, Devin, Claude Code, or equivalent) to improve developer productivity and reduce development cycle time.
- Apply AI tools to accelerate: code generation and refactoring test creation and coverage improvement debugging, root‑cause analysis, and performance optimization.
- Use AI‑assisted reasoning to understand complex codebases, rapidly prototype solutions, and improve code quality while maintaining strong engineering standards.
- Partner with peers and reviewers to validate, harden, and productionize AI‑generated outputs ensuring correctness, security, maintainability, and regulatory compliance.
- Identify opportunities where AI tooling can reduce manual effort, minimize rework, and support faster, higher‑quality delivery to production.