Senior GenAI Engineer (Hybrid) :: W2 Role Only
About The Position
The Senior GenAI Engineer designs, develops, and deploys scalable Generative AI solutions in enterprise environments. The role requires hands-on expertise in LLM-based applications, GenAI architectures, and cloud-native engineering practices, and partners with cross-functional teams to deliver production-ready AI systems that meet enterprise standards for scalability, security, and reliability. The position develops GenAI applications using large language models, retrieval-augmented generation, prompt engineering, and agentic AI concepts. It supports the GenAI lifecycle from design and development through integration, deployment, optimization, monitoring, and operational support. The engineer builds and deploys systems across Azure and/or AWS, contributes to distributed system design and infrastructure practices, and develops scalable Python applications using microservices architectures. The role also contributes to secure coding, data handling, CI/CD, automated testing, deployment workflows, and code reviews.
Requirements
- 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 with GenAI systems, including LLMs and RAG architectures.
- Understanding of agentic AI concepts and exposure to frameworks such as LangChain or LangGraph.
- Experience with cloud platforms, Azure and/or AWS.
- Knowledge of distributed systems and scalable application design.
- Proficiency in Python development.
- Experience with Docker, Kubernetes, and infrastructure-as-code tools.
Nice To Haves
- Experience deploying GenAI or ML solutions in production environments.
- Familiarity with observability and monitoring tools.
- Understanding of AI governance, compliance, and security practices.
- Experience in financial services or other regulated industries.
Responsibilities
- Develop and implement GenAI applications using LLMs, RAG architectures, prompt engineering, and agentic AI concepts and workflows.
- Build intelligent pipelines using frameworks such as LangChain, LangGraph, and Microsoft Foundry Agent Service.
- Evaluate the performance, accuracy, and scalability of GenAI implementations.
- Contribute to the end-to-end GenAI lifecycle, including solution design, development, integration, deployment, performance tuning, and optimization.
- Support secure deployment, horizontal scaling, and operational stability of GenAI workloads.
- Assist in implementing monitoring, logging, and observability practices for production environments.
- Develop and deploy GenAI systems across Azure and AWS cloud platforms.
- Contribute to distributed system design for scalable AI workloads.
- Use containerization, orchestration, and infrastructure-as-code practices, including Docker, Kubernetes, Terraform, and ARM/Bicep.
- Ensure solutions meet enterprise expectations for availability, performance, and security.
- Develop scalable applications using Python and microservices-based architectures.
- Apply secure coding standards and appropriate data handling practices for enterprise and regulated environments.
- Contribute to CI/CD pipelines, automated testing, and deployment workflows.
- Participate in code reviews and follow engineering best practices.
- Collaborate across engineering and product teams, take ownership, and deliver against commitments.
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What This Job Offers
Career Level
Senior
