Back End Engineer (Python + GCP + Gen AI)

You will work at the intersection of Software Engineering and Applied AI, developing intelligent agents that integrate with enterprise systems, automate workflows, and deliver secure, auditable, and trustworthy outputs. This role requires close collaboration with business and engineering teams to create impactful AI-driven solutions that support day-to-day operations. Key Responsibilities

• Design and develop LLM-powered agents to automate business and operational workflows, including document processing, data validation, and exception handling.
• Build integrations between AI agents and internal systems using APIs, databases, enterprise tools, and MCP servers.
• Implement guardrails, validation frameworks, and human-in-the-loop workflows to ensure reliable and trustworthy outputs.
• Develop production-grade AI applications with strong observability, logging, monitoring, auditability, and cost tracking.
• Partner with stakeholders to identify automation opportunities and convert requirements into scalable AI solutions.

• Build reusable AI engineering frameworks, including:

• Prompt management
• Context orchestration
• Output validation
• Evaluation and testing frameworks
• Stay updated on emerging LLM technologies, tools, and APIs to recommend practical architecture and model choices.

• Collaborate with architecture and engineering teams to ensure secure integration with enterprise platforms, authentication, and governance standards., Languages: Python, Java LLM Platforms: Claude, OpenAI, Enterprise AI Models Integrations: MCP Servers, REST APIs Databases: PostgreSQL, SQL Server Cloud/Infrastructure: AKS, Enterprise Data Platforms DevOps: GitHub Actions, CI/CD Pipelines Monitoring: Logging & Observability Tools Why Join Us?

• Work on high-impact AI automation initiatives within Finance and Operations.
• Build production-grade AI systems used by enterprise teams daily.
• Collaborate with experienced engineering and business leaders.
• Opportunity to contribute in a high-ownership role with a right-to-hire path., Job Description: Note: Fidelity will not provide immigration sponsorship for this position. The Role As a Senior Full Stack Developer in a Quantitative Development team, you …
• 1 day ago, Job Description: Note: Fidelity will not provide immigration sponsorship for this position. The Role As a Senior Full Stack Developer in a Quantitative Development team, you …
• 1 day ago +

We are seeking an experienced AI Engineer to design and build LLM-powered automation solutions that improve operational workflows across Finance and Business Operations. This is a hands-on engineering role, focused on building reliable, scalable, and production-ready AI systems-not research., * 7+ years of software engineering experience, including 2+ years building AI/LLM-based production applications.

• Strong hands-on expertise in Python and Java for enterprise application development.
• Experience working with LLM APIs such as OpenAI, Claude, or similar platforms.

• Strong understanding of:

• Prompt Engineering
• Function/Tool Calling
• Structured Outputs
• Context Management

• Experience building AI Agents / LLM workflows, including:

• Tool integrations
• API orchestration
• Multi-step workflows
• MCP server integration

• Strong knowledge of AI reliability and safety patterns, including:

• Output validation
• Hallucination mitigation
• Cost optimization
• Rate limiting
• Audit logging
• Experience integrating enterprise systems using REST APIs, SQL databases, and messaging systems.

• Strong software engineering fundamentals:

• Clean coding practices
• Testing and debugging
• CI/CD
• Observability and monitoring
• Excellent communication skills with the ability to explain AI capabilities to technical and non-technical stakeholders.

Preferred Qualifications

• Experience with RAG (Retrieval-Augmented Generation) pipelines.
• Knowledge of Vector Databases and document processing systems.
• Familiarity with LLM evaluation frameworks, regression testing, and human review systems.
• Experience in Financial Services, Operations, or highly regulated environments.
• Exposure to workflow orchestration tools such as Temporal.