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Mid-level (3–5yrs) GenAI role in a metro with broad in-demand skills increases candidate competition.
Specialized GenAI, LLM fine-tuning, vector DB and MLOps skills reduce cross-industry transferability.
Explicit 3–5 years and many mandatory GenAI, MLOps, and LLM tool skill requirements.
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Develop and implement advanced AI/ML models focusing on Generative AI, NLP, and Retrieval-Augmented Generation (RAG) architectures.
Utilize frameworks like LangChain, PyTorch, TensorFlow, HuggingFace, and manage vector databases for production-level solutions.
Oversee model evaluation, deployment pipelines including CI/CD, and contribute to MLOps processes on cloud platforms.
3 to 5 years of professional experience in AI/ML development.
Strong proficiency in Python and core AI/ML frameworks: LangChain, PyTorch, TensorFlow, HuggingFace.
Practical experience with cloud platforms, SQL, CI/CD pipelines, MLflow, and vector databases.
Preferably experience in LLM fine-tuning (LoRA/QLoRA) and familiarity with multi-agent AI frameworks like AutoGen and LangGraph.
Experience building and deploying generative AI and RAG-based solutions in production environments.
Technically adept working with advanced model fine-tuning and vector search implementations.
Comfortable operating within cloud-based MLOps setups and sophisticated AI frameworks for scalable AI solutions.