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Strong brand and metro location increase competition, but seniority and niche AI skills lower applicant density.
Highly domain-specific AI, LLM, and data engineering expertise limits cross-industry transferability.
Explicit 10+ years plus deep mandatory AI, ML, and engineering stack implies highly strict shortlisting.
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Design, develop, optimize, and maintain ETL processes and data pipelines using Python and SQL to support data warehousing and analytics.
Develop, deploy, and integrate advanced AI systems including agentic AI solutions leveraging large language models (e.g., ChatGPT, Claude) and AI engineering methodologies.
Implement and maintain robust APIs, ensure application resiliency, security, and operational stability for complex AI and data applications using modern software development practices and architectural patterns.
Minimum 10+ years of hands-on application development experience.
Strong proficiency in Python, including frameworks like FastAPI, Flask, PySpark, and scripting for data engineering.
Extensive experience with databases such as Oracle, Postgres, MongoDB, and ability to write complex SQL and PL/SQL scripts.
Experience with AI system design and integration including knowledge of AI concepts, frameworks (e.g., LangChain, AutoGen), and modern software engineering best practices (CI/CD, version control, testing).
Senior-level technologist skilled in engineering scalable enterprise AI and data solutions with deep expertise in Python and database systems.
Experienced in designing microservices and event-driven architectures with API-First Design, containerization using Docker and Openshift, and agile development methodologies.
Strong domain knowledge in AI concepts including large language models, MCP integration into agentic AI, and practical application of AI engineering and prompt engineering techniques.