Python Fullstack Developer

Role Summary

The Senior Software Engineer will lead the design, development, testing, deployment, and

ongoing support of enterprise-grade software applications, with meaningful opportunities

to contribute to AI-enabled and agent-based platform capabilities. This role requires

advanced full-stack and cloud engineering experience, strong backend and architecture

skills, and the ability to deliver scalable, secure, production-grade solutions in collaboration

with product, business, engineering, security, legal, and compliance stakeholders.

Key Responsibilities

• Design, develop, enhance, and maintain enterprise software applications and services

using Python, TypeScript/JavaScript, React, and modern backend frameworks.

• Architect, build, and operate RESTful APIs and microservices that support scalable,

production-grade business and platform capabilities across GCP and Azure.

• Apply microservice architecture patterns, including service decomposition, inter-

service communication, API gateway patterns, and event-driven design.

• Design reliable data models and optimize relational database solutions using

PostgreSQL; evaluate NoSQL and vector database options where appropriate, and work

with BigQuery/GCS for analytical and classification-tagged data.

• Implement secure application patterns, including authentication, authorization, input

validation, rate limiting, and secrets management.

• Own containerized deployment practices using Docker, Kubernetes, GKE, Cloud Run,

and Azure Container Apps, including production-readiness and operational support.

• Build and maintain CI/CD pipelines using GitHub Actions, Jenkins, or comparable

platforms, extending them where appropriate with AI-specific stages such as eval gates,

prompt-injection/red-team checks, and cost-budget checks.

• Instrument services and agents for observability using structured logging, distributed

tracing, and monitoring (e.g., OpenTelemetry, Grafana), applying redaction and privacy

controls where sensitive data is involved.

• Lead practical integration of LLMs into production applications, including prompt

engineering, retrieval-augmented generation (RAG), embeddings, and model evaluation

practices, routed through approved internal AI gateways.

• Contribute to agent-based capabilities where applicable, including Model Context

Protocol (MCP) server integration, agent lifecycle management, and non-human identity

patterns.

• Mentor engineers, guide technical delivery, and partner with product, business, security,

legal, and compliance stakeholders to translate requirements and governance needs

into effective technical solutions.