Description

Architect and implement end-to-end Generative AI and Agentic AI solutions.
Design enterprise-grade solutions using LLMs, RAG, multi-agent workflows, and vector databases.
Build scalable AI architectures on Microsoft Azure or Google Cloud Platform (GCP).
Lead GenAI solutions from POC/prototype through production deployment.
Integrate GenAI solutions with enterprise applications, APIs, data platforms, and cloud services.
Establish MLOps/LLMOps practices for deployment, monitoring, and lifecycle management
Design solutions considering enterprise security, scalability, governance, reliability, and integration requirements.

Must-Have Skills:
8–10+ years of experience in Software Engineering, Data Science, Machine Learning, or AI
3+ years of experience as a Solution Architect/System Architect.
Strong enterprise-level experience architecting and deploying GenAI solutions in large-scale production environments.
Hands-on experience with Generative AI, LLMs, RAG, and Agentic AI/Multi-Agent architectures.
Strong programming experience in Python.
Experience with LangChain, LlamaIndex, CrewAI, AutoGen, or similar frameworks.
Strong cloud architecture experience with Microsoft Azure or GCP.
Hands-on experience with vector databases such as Pinecone, Milvus, Qdrant, or PGVector
Strong understanding of MLOps/LLMOps, model deployment, monitoring, and productionization.
Experience with SQL, PySpark, distributed computing, and API design.
Experience with PyTorch or TensorFlow

Key Skills

Education

Bachelor's degree