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Job Description
Job Title:
AI/ML Engineer/Architect Location:
Frisco, TX Duration:
/
Term:
6+ months -
Contract Job Description:
Experience Desired:
10+ Years.
Qualification:
We are seeking a Hands-On Architect/Principal Software Engineer with 10+ years of experience in designing, developing, and deploying large-scale distributed applications, along with proven expertise in AI/ML and Agentic AI solutions. The ideal candidate should have strong hands-on coding experience in Python (mandatory) and at least one additional programming language such as Java, Go, Rust, or C++. Candidates must have experience building and deploying production-grade AI/ML applications, LLM-based systems, multi-agent architectures, and end-to-end MLOps pipelines.
Responsibilities:
Lead the architecture, design, development, and deployment of scalable, high-performance AI/ML and Agentic AI applications with a strong hands-on coding approach. Design and build cloud-native, distributed microservices and full-stack applications using Python and modern programming languages on AWS and Google Cloud Platform. Develop, deploy, and optimize production-ready LLM-based, multi-agent AI systems and end-to-end MLOps pipelines. Architect and implement Kubernetes-based infrastructure using Docker, Helm, ArgoCD, Istio/Linkerd, Cilium, and cloud-native networking best practices. Collaborate with product managers, data scientists, and engineering teams to translate business requirements into scalable technical solutions. Provide technical leadership, mentor engineering teams, conduct code reviews, and establish software engineering best practices. Drive AI-assisted software development by leveraging tools such as GitHub Copilot, ChatGPT, Claude, and other developer productivity solutions. Design and optimize REST APIs, gRPC services, databases, and distributed systems for high availability, scalability, and low latency. Implement CI/CD pipelines, DevOps automation, monitoring, observability, and security best practices across cloud environments. Troubleshoot complex production issues, optimize system performance, and continuously improve application reliability and operational excellence.
