Engineering - Software Engineer - Associate - London

Requirements

• Bachelor's or Master’s degree in Computer Science, Software Engineering, or a related quantitative field.
• 5+ years of hands-on software engineering experience, with a proven track record of building and deploying robust applications, and significant experience integrating AI/ML models.
• Strong proficiency in programming languages such as Python, Java, or Go, along with experience integrating with relevant AI/ML frameworks (e.g., TensorFlow, PyTorch).

What You'll Be Doing

• Rapid Prototyping & Application Development: Lead the end-to-end development of applications that integrate and leverage AI/ML models, from architectural design, data schema design, data pipeline construction, and rapid prototyping to initial deployment and operationalization, utilizing cloud-native services (e.g., serverless, containerization, managed AI/ML platforms) and CI/CD pipelines for accelerated delivery.
• Business Partnership & Solution Architecture: Collaborate closely with business and engineering teams to deeply understand their challenges and customer needs, identify high-impact opportunities to integrate AI capabilities into applications, and translate business requirements into robust cloud-optimized application architectures, scalable data models, and technical specifications for AI-powered solutions.
• Solution Implementation & Delivery: Architect, implement, and deliver scalable, robust, and maintainable cloud-native AI applications that consume and operationalize AI solutions based on defined technical specifications and architectures, ensuring seamless integration with existing systems and workflows within the Goldman Sachs ecosystem.
• Knowledge Transfer & Enablement: Facilitate effective knowledge transfer through comprehensive documentation, training sessions, mentorship, and pair-programming, empowering receiving teams to take ownership and continue the development and maintenance of AI-powered applications.
• Technology & Innovation Leadership: Stay abreast of the latest advancements in application development, system integration, AI/ML technologies, data management platforms, and operational best practices, continuously evaluating and recommending new tools, techniques, and architectural patterns to drive innovation in AI application delivery.

Nice to Haves

• Productionize LLMs: Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations
• Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability
• Collaborate directly with users: Partner with production engineers, and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability, risk reduction, and cost; and deliver auditable, business-aligned outcomes
• Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool-calls to meet stringent SLOs under real-world load

Perks and Benefits

• At Goldman Sachs, fostering and advancing diversity and inclusion in the workplace is a top priority.
• Training and development opportunities, firmwide networks, benefits, wellness, personal finance offerings, and mindfulness programs are provided to help individuals grow professionally and personally.