Generative AI Engineer

Virtusa; Irving (TX); On-site; USD 150,000 - 230,000; Full time

Job summary

Virtusa seeks a senior Python/GenAI engineer to design, implement, and govern LLM-based solutions in hybrid/cloud environments. You will work across microservices, APIs, and distributed systems to deliver secure, scalable AI-enabled applications and ensure production readiness with CI/CD and observability.

You will review AI-generated code, optimize performance, and manage cost efficiency while upholding enterprise security standards and governance practices.

Qualifications

• 10–12 years hands-on Python experience.
• Proven experience delivering LLM/GenAI solutions (RAG, orchestration, prompt engineering).
• Strong understanding of microservices, APIs, distributed systems, and enterprise integration patterns.
• Experience reviewing and governing AI-assisted/GenAI-generated code.
• Strong secure coding practices and performance optimization.
• Experience with cloud-native or hybrid environments, containers and orchestration platforms.
• Exposure to CI/CD pipelines, observability, and production support models.
• Understanding of AI governance concepts such as bias, explainability, auditability, and model risk controls.

Skills

Python

LLM / GenAI

Microservices

APIs

Distributed systems

AI governance

Secure coding

Performance optimization

Containerization / cloud-native

CI/CD / Observability / Production

Cost optimization

Job description

Strong hands‑on experience (10-12 years) in Python(mandatory).

Proven experience designing and delivering LLM / GenAI solutions (e.g., RAG, orchestration, prompt engineering, AI‑assisted automation).

Solid understanding of microservices, APIs, distributed systems, and enterprise integration patterns.

Experience reviewing and governing AI‑assisted / GenAI‑generated code.

Strong foundation in secure coding practices and performance optimization

AI & GenAI Experience designing and implementing LLM‑based solutions (RAG, agents, copilots, automation).

Understanding of model lifecycle, inference pipelines, performance tuning, and cost optimization.

Familiarity with AI governance concepts such as bias, explain ability, auditability, and model risk controls.

Experience with cloud‑native or hybrid environments, containers, and orchestration platforms.

Exposure to CI/CD pipelines, observability, and production support models.

Knowledge of secure coding practices and enterprise security standards.