Senior AI Platform Engineer

Apollo Solutions; Boston (MA); On-site; USD 140,000 - 190,000; Full time

Job summary

Apollo Solutions in Boston partners with a leading financial services firm to hire a Senior AI Platform Engineer who will scale and evolve its production AI platform. You will design and build scalable backend services, manage Kubernetes workloads, implement CI/CD, and ensure security, logging, and observability to support AI at scale across the organization.

You will collaborate with cloud, security, and application teams to deliver production-ready, reliable AI capabilities across the business.

Qualifications

• 7+ years in software/platform/infrastructure engineering or related field.
• Strong software engineering experience using Python or TypeScript.
• Experience designing, building, and operating production-grade applications or platform services.
• Experience developing backend services, APIs, and distributed systems.
• Solid understanding of cloud platforms, preferably AWS or Azure.
• Hands-on experience with Kubernetes and containerized environments.
• Experience with Infrastructure as Code and CI/CD automation.
• Experience building highly available, secure, and resilient systems.
• Strong knowledge of monitoring, logging, observability, and operational best practices.
• Experience troubleshooting production incidents and performance issues.

Responsibilities

• Design, build, and enhance a production AI platform supporting 1,000+ users.
• Develop scalable backend services, APIs, and platform components for AI-enabled apps.
• Build services managing interactions between apps, LLMs, and AI providers.
• Implement secure integrations between AI apps, enterprise systems, and data sources.
• Develop reusable tooling and platform capabilities to accelerate AI app development.
• Improve platform architecture, performance, reliability, and scalability as adoption grows.
• Deploy, operate, and support AI services in production environments.
• Maintain Kubernetes-based workloads and containerized platform services.
• Automate infrastructure provisioning, deployment, and operational processes (IaC, CI/CD).
• Implement monitoring, logging, observability, and alerting across services.
• Troubleshoot and resolve production performance and reliability issues.
• Collaborate with cloud, security, and app engineering teams to deliver production-ready AI capabilities.
• Implement authentication, authorization, and security controls across platform services.
• Evaluate emerging AI technologies and contribute to platform architecture decisions.

Skills

Python

TypeScript

Kubernetes

AWS

Azure

Distributed Systems

CI/CD

Observability

Security

AI Infra

Job description

We are partnering with a financial services company based in Boston that is continuing to invest heavily in its AI capabilities and is seeking a Senior AI Platform Engineer to help scale and evolve its production AI platform.

The organization already operates an AI platform supporting more than 1,000 users across the business. This role will focus on building new platform capabilities, improving platform architecture, and ensuring the platform remains secure, reliable, and scalable as AI adoption continues to grow.

You'll be working on real world production systems, helping solve the engineering challenges that come with operating AI at scale while delivering new capabilities used across the organization.

Key Responsibilities

• Design, build, and enhance a production AI platform supporting more than 1,000 users.
• Develop scalable backend services, APIs, and platform components that power AI-enabled applications across the business.
• Build services that manage interactions between applications, large language models, and AI providers.
• Design and implement secure integrations between AI applications, enterprise systems, and internal data sources.
• Develop reusable tooling, frameworks, and platform capabilities that accelerate the development of AI-powered applications.
• Improve platform architecture, performance, reliability, and scalability as platform adoption grows.
• Deploy, operate, and support AI services in production environments.
• Build and maintain Kubernetes based workloads and containerized platform services.
• Automate infrastructure provisioning, deployment, and operational processes using Infrastructure as Code and CI/CD best practices.
• Implement monitoring, logging, observability, and alerting across AI platform services.
• Troubleshoot and resolve performance, scalability, and reliability issues across production environments.
• Work closely with cloud, infrastructure, security, and application engineering teams to deliver production ready AI capabilities.
• Implement authentication, authorization, and security controls across platform services.
• Evaluate emerging AI technologies and contribute to platform architecture decisions and technical improvements.
• Support the adoption of engineering best practices across AI platform development.

Required Qualifications

• 7+ years of experience in software engineering, platform engineering, infrastructure engineering, or a related field.
• Strong software engineering experience with Python or TypeScript.
• Experience designing, building, and operating production-grade applications or platform services.
• Experience developing backend services, APIs, and distributed systems.
• Strong understanding of cloud platforms, ideally AWS or Azure.
• Hands-on experience with Kubernetes and containerized environments.
• Experience with Infrastructure as Code and CI/CD automation.
• Experience building and supporting highly available, secure, and resilient systems.
• Strong knowledge of monitoring, logging, observability, and operational best practices.
• Experience troubleshooting production incidents and performance issues.
• Understanding of generative AI, large language models, or AI infrastructure.
• Experience integrating AI services into enterprise applications.
• Strong collaboration and communication skills with the ability to work across multiple engineering teams.

Preferred Qualifications

• Experience building or supporting enterprise AI platforms.
• Experience with AWS AI Services, Azure AI Services, or other cloud AI offerings.
• Knowledge of Retrieval-Augmented Generation (RAG), AI agents, model orchestration, and agentic workflows.
• Exposure to Model Context Protocol (MCP) and AI tool integrations.
• Experience supporting applications that utilize multiple AI models or providers.
• Familiarity with AI monitoring, evaluation, governance, and model lifecycle management.
• Experience developing internal developer platforms or shared engineering services.
• Experience working within financial services, regulated industries, or large-scale enterprise environments.