Responsibilities
- Develop and implement GenAI applications leveraging Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG) architectures
- Prompt engineering techniques
- Agentic AI concepts and workflows
- Build intelligent pipelines using frameworks such as LangChain, LangGraph, and Microsoft Foundry Agent Service
- Evaluate solution performance, accuracy, and scalability of GenAI implementations
- Contribute to the end-to-end GenAI lifecycle, including Solution design and development
- Integration and 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 cloud platforms (Azure and AWS)
- Contribute to distributed system design for scalable AI workloads
- Utilize modern infrastructure practices: Containerization (Docker)
- Orchestration (Kubernetes)
- Infrastructure as Code (Terraform, ARM/Bicep)
Qualifications
- Bachelor’s degree in Computer Science, Engineering, or related field
- 5–8 years of experience in software or platform engineering
- 2+ years hands‑on experience with GenAI systems, including LLMs and RAG architectures and vector databases
- 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
- 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 is a plus
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