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Tier-1 employer and metro location increase competition, though senior niche LLM focus reduces applicant density.
Specialized ML/LLM engineering skills are transferable across industries but require deep AI experience.
Explicit 12–18 years plus numerous mandatory LLM, deployment, and vector DB skills make filters highly strict.
Design, architect, and develop scalable production-grade ML and LLM solutions including agentic and multi-agent systems integrated via REST APIs and FastAPI.
Establish end-to-end AI pipelines covering data ingestion, transformation, embedding generation, vector database integration, and system observability including drift detection and response quality analysis.
Lead technical efforts within Agile Scrum teams, collaborate cross-functionally to align AI strategies with business goals, mentor junior members, and drive adoption of AI agents and automation frameworks.
12 to 18 years of relevant experience, with minimum 6 years specifically in Data Science.
Proficiency in Python, Machine Learning, REST API development, FastAPI, and SQL.
Experience designing and deploying production-grade ML and LLM systems with strong background in data processing, statistical modeling, and large language models like GPT or BERT.
Familiarity with agent frameworks (e.g., LangChain), vector databases, observability tools for AI systems, and microservices architecture.
Senior technical leadership experience in building complex enterprise AI/ML solutions including large-scale LLM-based systems and multi-agent workflows.
Experienced in operating within Agile Scrum environments and collaborating closely with cross-domain teams to translate business needs into AI strategies.
Skilled at pioneering AI automation using agentic systems and orchestrations, mentoring teams, and setting best practices in ML/LLM engineering.