Platform Development And Ai Engineering

Role: Platform Development and AI Engineering
Location: Alpharetta, GA (Mini 3days in Office)

Senior Python Developer with Architect knowledge and experience to base AI Engineering atleast, need to go for 3 rounds of Client interview.

Mandatory Skills:

Full Stack Development and Architecture knowledge

Python

React

DevOps

AI Engineering: Langchain, LangGraph, AI Platform, Agent Building, A2A Communications etc.

We are seeking a hands-on Platform Engineer with strong full-stack software development and AI engineering skills to join the CPS PEArch team.

PEArch team designs, builds, and governs scalable, secure, and resilient platform capabilities that support critical Wealth Management applications. Its mission is to turn architectural standards into reusable engineering solutions, platform services, AI agents, and automation that application teams can adopt at scale.

This is a software engineering role with architecture responsibilities-not solely an infrastructure administration or architecture-review position. The candidate will own solutions from design and coding through testing, deployment, observability, and production support. Candidate will collaborate with Core Platform Engineering, application development, embedded SRE, cybersecurity, infrastructure, and production-management teams to improve reliability, accelerate delivery, and strengthen engineering standards.

Key Responsibilities

Full-Stack Software and Platform Development

Design, develop, test, and operate production-grade APIs, backend services, web applications, and developer-facing platforms. Build self-service portals, infrastructure platform catalogs, reusable components, and automation that simplify adoption of approved platform capabilities.

Develop integrations across application services, databases, messaging platforms, API gateways, and enterprise engineering tools.

Write maintainable, secure, well-tested code and participate in code reviews, design discussions, troubleshooting, and performance optimization.

Own capabilities throughout the SDLC, including CI/CD, deployment, rollback, documentation, and operational readiness. AI Engineering and Intelligent Automation

Build AI agents and AI-assisted workflows for architecture analysis, engineering knowledge discovery, evidence collection, standards validation, and remediation planning.

Develop retrieval-augmented generation solutions that ground responses in authorized architecture documents, code, configuration, and platform knowledge.

Integrate agents with enterprise APIs and tools through controlled interfaces, including Model Context Protocol (MCP) where appropriate.

Implement evaluation frameworks for accuracy, grounded ness, safety, consistency, latency, and cost.

Engineer production controls for AI systems, including access restrictions, bounded execution, auditability, human approval, and safe fallback behavior.

Automate architecture-review feedback and remediation tracking within developer workflows.

Resilience, Scalability, and Operational Excellence

Identify and remediate single points of failure, shared-resource risks, scaling bottlenecks, and critical dependency weaknesses.

Implement timeouts, bounded retries, circuit breakers, bulkheads, idempotency, backpressure, and graceful degradation where appropriate.

Design and validate high-availability, failover, disaster-recovery, and data-reconciliation capabilities aligned with business requirements.

Build observability through metrics, structured logs, distributed tracing, dashboards, and actionable alerts.

Partner with SRE and application teams on load testing, failure testing, incident analysis, and recovery validation.

Architecture Standards and Platform Governance

Translate architectural patterns and standards into reusable libraries, reference implementations, automated checks, and engineering guardrails.

Develop and maintain the infrastructure platform catalog, adoption guidance, and architecture decision records.

Detect deviations from approved designs and service-level objectives and help teams prioritize corrective actions.

Promote consistent API design, secure integration, platform reuse, and measurable production-readiness criteria.

Share engineering practices through demonstrations, documentation, mentoring, and PEArch Guild sessions.

Required Qualifications

Strong hands-on software development experience using Java, Python, C#/.NET, or comparable backend technologies.

Experience developing user interfaces with React, Angular, TypeScript, or an equivalent modern framework.

Demonstrated ownership of production applications or platform services, from design through operational support.

Experience designing APIs, distributed systems, asynchronous integrations, and relational or NoSQL data access.

Practical experience developing LLM-enabled applications, AI agents, retrieval workflows, or AI-powered automation.

Experience with containers, Kubernetes or equivalent platforms, CI/CD, and infrastructure/configuration as code.

Strong understanding of authentication, authorization, secrets management, secure coding, and data protection.

Ability to diagnose application, database, network, and dependency-related performance or reliability issues.

Strong collaboration skills and the ability to explain technical decisions and trade-offs to diverse stakeholders.

Preferred Qualifications

Experience with Wealth Management, banking, trading, or other high-availability transaction-processing systems.

Familiarity with API gateways such as Apigee, messaging platforms such as Kafka or MQ, and distributed caching.

Experience with MCP integrations, agent orchestration, vector search, AI evaluation, and production AI monitoring.

Knowledge of multi-data-center architecture, disaster recovery, SLOs, and resilience testing.

Experience building internal developer platforms, service catalogs, policy-as-code, or automated architecture-governance capabilities.