Description

Key Responsibilities

Lead the architecture, design, development, testing, and deployment of scalable software applications and cloud-native solutions.

Evaluate and implement emerging technologies, including Generative AI, LLMs, agentic AI, RAG, APIs, automation, and data-driven applications.

Translate business requirements into technical designs, architecture patterns, roadmaps, and implementation plans.

Develop secure, scalable, maintainable applications using modern programming languages, APIs, microservices, and distributed systems.

Guide and mentor engineers, conduct design reviews, and establish reusable engineering standards and best practices.

Collaborate with product, architecture, cybersecurity, infrastructure, data, and business teams.

Establish proof-of-concept success criteria, automated testing, performance metrics, monitoring, and production readiness standards.

Identify technical risks, security concerns, integration dependencies, and operational challenges throughout the SDLC.

Support responsible AI adoption, including model evaluation, governance, data privacy, security, and compliance.

Required Qualifications

8+ years of progressive software engineering experience, including hands-on development of complex applications, APIs, distributed systems, or cloud-native platforms.

Strong proficiency in one or more languages: Python, Java, JavaScript/TypeScript, C#, Go, or similar.

Experience leading technical delivery across the full SDLC, from requirements and architecture through development, testing, and deployment.

Strong knowledge of software architecture, API integration, automated testing, CI/CD, DevOps, and production support.

Working knowledge of cloud platforms, containerization, infrastructure automation, and modern integration patterns.

Excellent analytical, problem-solving, decision-making, communication, and technical leadership skills.

Preferred Qualifications

Experience with Generative AI, LLMs, agentic workflows, RAG, vector databases, machine learning, or prompt engineering.

Experience moving emerging technology solutions from proof of concept to production.

Knowledge of responsible AI, model evaluation, guardrails, cybersecurity, data privacy, and enterprise governance.

Familiarity with AWS, Azure, or GCP; Kubernetes; serverless computing; microservices; event-driven architecture; and infrastructure-as-code.

Experience mentoring engineers and influencing technical direction across teams.

Cloud, AI/ML, or Software Architecture certifications are a plus

Key Skills

Education

Bachelor’s degree