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Strong Tier-1 brand and mid-level title increase competition, but specialized LLM and graph requirements limit applicant pool.
Core ML and LLM skills are transferable, but agentic AI and knowledge-graph experience add domain specificity.
Explicit 5+ years and mandatory LLM, graph, cloud, and MLOps skills make shortlisting highly strict.
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Own and execute AI/ML problem statements and domain-specific LLM evaluation, fine-tuning, and operationalization within the Agentic AI platform initiative.
Design and implement evaluation frameworks, knowledge graphs, embeddings, and scalable inference engines for semantic search and reasoning.
Collaborate with engineering to deploy production-scale AI solutions and communicate findings across business and product teams.
Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field.
5+ years overall IT experience with at least 3 years in applied machine learning or NLP, including production environment experience.
Strong expertise in LLMs, GenAI, NLP, fine-tuning, evaluation, Python, and ML libraries such as PyTorch, TensorFlow, Scikit-learn.
Experience with graph databases (e.g., Neo4j), knowledge graphs, cloud AI/ML services (AWS, Azure, or GCP), and MLOps practices; On-premise work location.
Experienced in leading applied research and product-driven AI/ML projects focused on LLMs and agentic AI solutions.
Comfortable working cross-functionally with engineering, product management, and business teams to deploy scalable AI systems.
Demonstrated ability in building and maintaining evaluation metrics, knowledge graphs, and inference engines for advanced AI applications.