PRX – AI Engineer

Role Overview

Goldman Sachs is seeking an experienced PRX AI Engineer to design build and deploy GenAI and Agentic AI solutions that improve reliability reduce risk and lower operational costs in large-scale production environments. This role focuses on building intelligent agents capable of diagnosing issues reasoning over complex systems and executing secure actions in production.

Note: No low-code / no-code experience is applicable for this role. Strong hands-on engineering is mandatory.

Key Responsibilities

Agentic AI & GenAI Development

• Design and implement agentic AI systems using tool-calling agents retrieval structured reasoning and secure action execution.
• Implement function calling policy enforcement and orchestration following MCP protocols.
• Engineer strong guardrails for safety compliance and least-privilege access.

LLM Production & Evaluation

• Productionize LLMs by building evaluation frameworks for open-source and foundational models.
• Develop retrieval pipelines prompt orchestration response validation and self-correction loops.
• Optimize LLM workflows for production operations and runtime environments.

Runtime & Ecosystem Integration

• Integrate AI agents with observability incident management CI/CD and deployment systems.
• Enable automated diagnostics runbook execution remediation and post-incident summaries with full auditability.

Collaboration & Business Impact

• Partner with production engineers and application teams to translate operational pain points into agentic AI solutions.
• Define success metrics aligned with reliability risk reduction and cost optimization.
• Deliver traceable and auditable outcomes.

Safety Governance & Reliability

• Implement validator models adversarial testing policy checks and deterministic fallbacks.
• Build circuit breakers rollback mechanisms and continuous evaluation pipelines.

Performance & Scalability

• Optimize latency and cost through prompt engineering caching model routing batching streaming and parallel tool calls.
• Ensure systems meet strict production SLOs.

RAG & Knowledge Systems

• Design and maintain RAG pipelines with domain knowledge curation.
• Build data quality validation frameworks and feedback loops to ensure knowledge freshness.

Engineering Excellence

• Lead design reviews and enforce high-quality engineering standards.
• Mentor peers on agent architectures evaluation methods and safe AI deployment practices.

Essential Skills & Experience

• 5 years of software development experience (Python preferred; Java Go C/C acceptable).
• 3 years designing and launching production ML systems including deployment monitoring evaluation and data pipelines.
• Hands-on experience with LLMs including:
  • API integrations
  • Prompt engineering
  • Fine-tuning / adaptation
  • RAG pipelines
  • Tool-using / function-calling agents
• Strong understanding of commercial and open-source LLMs (OpenAI Gemini Llama Qwen Claude etc.).
• Solid foundation in applied statistics ML concepts algorithms and data structures.
• Excellent problem-solving skills with strong ownership and communication abilities.

Preferred Skills

• Experience with AWS cloud infrastructure including:
  • ECS / EKS Lambda
  • S3 DynamoDB Redshift
  • Step Functions
  • SageMaker
• Infrastructure as Code using Terraform or CloudFormation.
• Experience operating AI systems in large-scale production environments.