Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Tier-1 brand, metro location and mid-level experience increase applicant density despite specialized GenAI skills.
High because role requires specialized GenAI, LLM orchestration, LangChain, vector DB, and cloud AI ecosystem expertise.
High due to explicit 4–7 years requirement and mandatory GenAI, cloud, and LLM toolchain experience.
Develop and deploy enterprise-grade Generative AI applications, APIs, and microservices focused on optimizing LLM orchestration, Retrieval-Augmented Generation (RAG) pipelines, and multi-agent autonomous workflows.
Integrate leading commercial and foundational AI models via major enterprise cloud platforms (Azure OpenAI, AWS Bedrock, GCP Vertex AI) ensuring safe, compliant, and ethical AI deployments with performance monitoring.
Collaborate with internal leaders and external clients to rapidly prototype GenAI Proof of Concepts demonstrating capabilities and business impact.
4-7 years of software development experience with strong computer science fundamentals.
Expert proficiency in Python programming and RESTful/GraphQL API development using frameworks such as FastAPI, Flask, or Django.
Hands-on experience with at least one major cloud AI ecosystem: GCP Vertex AI, Azure OpenAI Service, or AWS Bedrock.
Proficiency in SQL and NoSQL databases, experience with frameworks like LangChain or LlamaIndex, and knowledge of vector databases/search engines such as Pinecone or Milvus.
Demonstrates advanced expertise in Generative AI system design including prompt engineering, embedding techniques, and chain-of-thought prompting.
Experienced in deploying AI models within enterprise cloud environments, including containerization (Docker) and CI/CD practices for production-scale AI solutions.
Capable of implementing Responsible AI practices including safety guidelines, content moderation, data privacy, and building LLM evaluation and monitoring frameworks.