Platform Engineer

• Design, architect, document, and lead the development of AI/ML platform components and agentic AI applications
• Implement and optimize cutting-edge AI/ML algorithms and production-quality code, primarily using Python and possibly Go, C, C++, or Rust
• Build and deploy container-based applications to platforms like Red Hat OpenShift, public clouds (AWS, GCP, Azure), leveraging CI/CD best practices and MLOps infrastructure
• Stay current with advancements in Generative AI and related technologies, conduct root cause analysis
• Architect and maintain data pipelines that feed training data, model artifacts, and inference logs into a governed data lake (S3, on prem object store)
• Design, implement, and operate a unified MLOps platform that supports both on-premises and commercial cloud platforms hosted Kubernetes clusters
• Work closely with research scientists, data scientists, product teams, and stakeholders

AI Platform Engineer

• Design, build, and deploy Microsoft Copilot Studio agents—including topics, knowledge sources, generative orchestration, authentication, and content moderation—that automate enterprise workflows across the bank
• Develop and maintain custom Power Platform connectors and MCP (Model Context Protocol) servers that securely expose internal banking systems and APIs to Copilot Studio agents and Power Platform makers
• Architect and govern Dataverse solutions—tables, relationships, security roles, business rules, plug-ins, and elastic tables—serving as the system of record for Copilot Studio agents and Power Apps
• Build agent tools, plug-ins, and reusable components in C# and .NET 10 to extend Copilot Studio with custom business logic, API integrations, and AI orchestration patterns (tool calling, RAG, declarative agents)
• Operate Power Platform Application Lifecycle Management (ALM) at scale—managed environments, solution packaging, the ALM Accelerator, Power Platform CLI (pac), and pipelines integrated with GitHub Actions
• Implement responsible AI governance—DLP policies, environment strategy, agent telemetry, evaluation harnesses, content safety, and grounding—ensuring compliance with bank security and regulatory standards
• Partner with software, data, and AI engineers to integrate Azure OpenAI, Azure AI Foundry, and Azure AI Search into Power Platform and Copilot Studio solutions, including knowledge ingestion and retrieval pipelines
• Mentor pro-code and citizen developers on Copilot Studio patterns, Power Platform ALM, secure coding, and AI engineering best practices; champion compliance with change management and SDLC standards.

• Provision and manage cloud infrastructure across compute, networking, storage, and managed databases
• Maintain consistency across development, staging, and production environments
• Plan and execute workload migrations across cloud providers with minimal disruption
• Identify and implement infrastructure cost optimization opportunities
• Build and maintain CI/CD pipelines across a multi-repository environment
• Implement deployment strategies such as blue/green, canary, and rolling deployments
• Develop automation tooling and scripts to reduce manual effort and improve delivery speed
• Write and maintain Terraform infrastructure across multi-account, multi-environment systems
• Establish IaC standards, module reuse patterns, and state management practices
• Drive infrastructure changes through version-controlled, peer-reviewed workflows
• Participate in on-call rotation and lead incident response efforts
• Maintain observability through dashboards, alerting, logging, and SLO tracking
• Conduct post-incident reviews and implement long-term remediations
• Manage secrets, certificates, and access controls using least-privilege principles
• Apply security best practices across infrastructure, containers, and CI/CD pipelines
• Support audit and compliance initiatives through documentation and access reviews
• Partner with engineering teams on infrastructure guidance and service design
• Create architecture documentation, runbooks, and operational guides
• Help build self-service platform capabilities for engineering teams

Senior Platform Engineer

• Design, build, and operate large-scale, on‑premises GPU infrastructure
• Lead advanced Linux system administration and platform initiatives
• Architect, deploy, and operate containerized GPU workloads using Kubernetes and related tooling
• Manage, optimize, and troubleshoot NVIDIA GPU environments at scale
• Support and improve cluster scheduling, capacity planning, and resource management
• Diagnose and resolve complex, cross-cutting, or poorly defined infrastructure issues
• Collaborate closely with application, research, and infrastructure teams to enable GPU workloads and drive platform adoption
• Take ownership of complex or ambiguous problems and drive them to resolution
• Provide technical guidance, mentoring, and occasional leadership on platform design and operational decisions
• Influence platform direction through hands-on expertise, architectural input, and operational insight
• Drive continuous improvements in platform reliability, scalability, performance, and operational excellence

Lead Software Engineer – GPU Platform Engineer

• Design, implement, and maintain Kubernetes-based deployment strategies for a multi-service application.
• Develop, manage, and version Helm charts supporting multiple environments (dev, test, staging, production).
• Ensure consistent deployments across on-prem (self-hosted) and cloud Kubernetes platforms.
• Implement secure configuration management (secrets, certificates, RBAC, network policies).
• Troubleshoot complex deployment, integration, and runtime issues across distributed systems.
• Serve as a primary escalation point for production issues, driving root cause analysis and resolution.
• Provide direct support to internal teams and/or customers, including issue triage, communication, and resolution tracking.
• Optimize cluster performance, resource utilization, and cost efficiency.
• Collaborate with development teams to improve containerization and deployment practices.
• Maintain system observability (logging, monitoring, alerting).
• Support compliance requirements (e.g., FIPS, STIG, hardened images, restricted networks).

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