Artificial Intelligence Engineer

Virtusa; New York (NY); On-site; USD 150,000 - 210,000; Full time

Job summary

Virtusa seeks a senior software architect with 8+ years of experience in software development and technical architecture, focusing on Python and AI-driven enterprise systems.

You will design end-to-end architectures embedding GenAI components (LLMs, RAG pipelines, embeddings) into core applications, lead architecture reviews, and guide deployment on cloud platforms (AWS/Azure/GCP) with strong governance of microservices and APIs.

Qualifications

• 8+ years of software development and technical architecture experience.
• Expert-level knowledge of Python, OOP, design patterns, data structures, and software engineering principles.
• Strong experience designing enterprise apps, microservices, REST APIs, and distributed systems.
• Strong understanding of AI/ML concepts, algorithms, model lifecycle, evaluation, and AI/ML architecture.
• Strong experience in GenAI, LLMs, RAG, embeddings, and vector databases.
• End-to-end solution architectures embedding GenAI components into core enterprise systems.
• Establish architecture standards, governance for microservices, APIs, and cloud-native design.
• Model application, data, and integration architectures for performance, security, and maintainability.
• Evaluate GenAI platforms/frameworks for fit, feasibility, and scalability.
• Conduct architecture reviews to ensure compliance with enterprise standards.
• Hands-on with PyTorch, TensorFlow, Scikit-learn; experience with FastAPI/Flask/Django.
• Strong SQL, databases, data processing, and data integration.
• Experience deploying on AWS, Azure, or GCP; knowledge of MLOps, CI/CD, Docker, Kubernetes.
• Integrate AI/ML capabilities with enterprise apps via APIs and microservices.

Responsibilities

• Design enterprise architectures leveraging GenAI components (LLMs, RAG pipelines, embeddings).
• Lead architecture reviews and design assessments for cloud-native, scalable systems.
• Evaluate and select GenAI platforms, frameworks, and tooling for long-term viability.
• Collaborate across teams to deploy AI-enabled enterprise applications with secure data flows.

Skills

Python

OOP

Design patterns

REST APIs

Distributed systems

GenAI concepts

LLMs

Embeddings

Vector databases

Cloud architecture

CI/CD

Docker/Kubernetes

Tools

PyTorch

TensorFlow

Scikit-learn

FastAPI

Flask

Django

Docker

Kubernetes

AWS

Azure

GCP

Job description

8+ years of experience in software development and technical architecture, with strong hands-on experience in Python.

Expert-level knowledge of Python, object-oriented programming, design patterns, data structures, and software engineering principles.

Strong experience designing enterprise applications, microservices, REST APIs, and distributed systems.

Strong understanding of AI/ML concepts, algorithms, model lifecycle, model evaluation, and AI/ML application architecture.

Strong experience in GenAI, LLMs, RAG, embeddings, and vector databases

Design end-to-end solution architectures embedding Generative AI components (LLMs, RAG pipelines, prompt engineering, AI agents) into core enterprise systems.

Establish architecture standards, design principles, and governance frameworks for microservices, APIs, and cloud-native application design.

Model application, data, and integration architectures to ensure performance, scalability, security, and maintainability.

Evaluate and select GenAI platforms, frameworks, and tooling based on architectural fit, technical feasibility, and long-term scalability.

Conduct architecture reviews and technical design assessments to ensure compliance with enterprise design standards

Hands-on experience with PyTorch, TensorFlow, Scikit-learn, or equivalent AI/ML frameworks.

Experience with Python frameworks such as FastAPI, Flask, or Django.

Strong knowledge of SQL, databases, data processing, and data integration.

Experience designing and deploying solutions on AWS, Azure, or GCP.

Understanding of MLOps, CI/CD, Docker, Kubernetes, model deployment, and monitoring.

Experience integrating AI/ML capabilities with enterprise applications using APIs and microservices