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Mid-level popular AI role with 5+ years and metro corporate posting yields high candidate competition.
Role requires deep GenAI, AgentOps, GPU and domain fine-tuning skills, making cross-industry transfer difficult.
Mandatory 5+ years, specific GenAI, Agent frameworks, GPU, and MLOps requirements enforce high shortlisting strictness.
Design, build, and deploy enterprise-grade Generative AI and Agentic AI solutions including RAG and GraphRAG applications.
Own end-to-end lifecycle from Proof of Concept to production deployment and ongoing optimization across cloud-agnostic (Azure, AWS, GCP) and on-premise environments.
Collaborate with automotive domain experts and business stakeholders to translate complex requirements into scalable AI applications incorporating fine-tuned open-source models.
5+ years of software engineering or AI/ML experience with hands-on expertise in Generative AI, Agentic AI, and production AI solutions.
Proficiency in Python with mandatory experience in FastAPI and Flask frameworks.
Experience designing and delivering cloud-agnostic AI solutions and hosting models/applications in on-premise environments with GPU and CUDA knowledge for efficient model hosting and fine-tuning.
Hands-on experience with agent frameworks (e.g., LangGraph, AutoGen, Semantic Kernel), containerization (Docker, Kubernetes), CI/CD, MLOps, LLMOps, and working with SQL/NoSQL databases.
Experienced in developing complex AI solutions integrating multi-agent systems and enterprise-grade RAG/GraphRAG architectures.
Comfortable leading full application lifecycles from PoC to production in hybrid cloud and on-premise settings with advanced knowledge of AI platform ecosystems.
Strong domain familiarity with automotive industry insights and expertise in parameter-efficient fine-tuning techniques like LoRA and QLoRA.