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Specialized GenAI skills but senior title and unknown brand produce moderate applicant competition.
Core ML/AI engineering skills transfer across industries, though GenAI specialization increases domain specificity.
Role mandates hands-on GenAI, LLM, deployment, vector DB and CI/CD experience, creating strict technical filters.
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Lead design, development, and delivery of AI/ML solutions focusing on Generative AI, LLMs, and agentic architectures.
Implement GenAI workflows including retrieval augmented generation (RAG) systems, fine tuning LLMs, prompt-driven agents, and automation pipelines.
Build proof of concept implementations and guide AI/ML best practices while collaborating with business stakeholders and ensuring responsible AI and compliance.
Hands-on experience in Python programming and practical implementation of Generative AI projects involving LLMs and domain-specific models.
Experience with ML libraries such as scikit-learn, TensorFlow, or equivalent is a definite plus.
Solid understanding of AI architecture including embeddings, vector databases, and experience with model deployment, monitoring, and CI/CD.
Familiarity with cloud AI platforms such as Azure, AWS, or GCP and associated data and ML services.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in driving end-to-end AI/ML projects with a focus on applying Generative AI and agentic frameworks in production environments.
Capable of building PoCs to validate AI concepts and shaping AI initiatives in alignment with business needs.
Comfortable working with cloud-based AI services and managing responsible AI practices including enterprise security and compliance considerations.