What are the responsibilities and job description for the Senior Engineer (Generative AI) position at Intone Networks Inc.?
Position: Senior Engineer – Generative AI
Duration: 4 Months Contract
Locations: - Atlanta, Bay Area, Charlotte, Chicago, Cincinnati, Dallas Metro, Denver, Milwaukee, New York, Portland, or St. Louis
The ideal candidate will have strong hands-on experience with LLMs, RAG, agentic AI, Python, cloud platforms, and cloud-native engineering and should be comfortable building production-ready AI applications with a focus on scalability, security, and reliability.
Key Responsibilities
Design and develop enterprise Generative AI applications using LLMs, RAG, prompt engineering, and agentic AI.
Build AI pipelines and workflows using frameworks such as LangChain, LangGraph, and Microsoft Foundry Agent Service.
Evaluate GenAI solutions for accuracy, performance, scalability, and reliability.
Support the complete GenAI lifecycle from design and development through deployment, optimization, and production support.
Implement monitoring, logging, observability, and operational practices for production GenAI workloads.
Develop and deploy AI solutions across Azure and/or AWS.
Design scalable distributed systems for AI workloads.
Use Docker, Kubernetes, and Infrastructure as Code tools such as Terraform and ARM/Bicep.
Develop scalable Python applications and microservices.
Contribute to CI/CD pipelines, automated testing, and deployment processes.
Apply secure coding and data-handling practices suitable for enterprise and regulated environments.
Participate in architecture/design discussions, code reviews, and engineering best practices.
Required Qualifications
Bachelor's degree in Computer Science, Engineering, or a related field.
5–8 years of experience in software engineering, AI/ML, or platform engineering.
Hands-on experience building Generative AI / LLM applications.
Strong experience with RAG architectures.
Understanding of agentic AI concepts and experience with frameworks such as LangChain or LangGraph.
Experience with Azure and/or AWS.
Strong Python development experience.
Understanding of distributed systems and scalable application architecture.
Hands-on experience with Docker and Kubernetes.
Experience with Infrastructure as Code tools such as Terraform, ARM, or Bicep.
Preferred Qualifications
Experience deploying GenAI/ML solutions into production.
Experience with GenAIOps, LLMOps, monitoring, and observability.
Knowledge of AI governance, security, compliance, and responsible AI practices.
Experience working in financial services or other regulated industries.