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Tier-1 brand, mid-level AI/data role with broad required skills and metro locations increases competition.
Core data and AI engineering skills are transferable, though GenAI and consulting context add some specificity.
Explicit 2-4 years plus mandatory data/GenAI stack, cloud, and vector DB experience tightens shortlisting.
Design, build, and maintain scalable data and AI solutions including data models, pipelines, and AI/LLM integrations such as RAG and semantic search to support enterprise analytics and AI applications.
Develop APIs, data ingestion, transformation, validation pipelines and implement retrieval pipelines using embeddings and vector search to improve LLM-powered applications.
Collaborate with cross-functional teams for translating business requirements into secure, scalable technical solutions while ensuring data quality, security, and responsible AI governance.
2-4 years of professional experience in data engineering, AI/ML engineering, or software engineering with hands-on experience building data-intensive or AI-enabled applications.
Proficient programming skills in Python; experience with ETL/ELT pipelines, relational databases (e.g., PostgreSQL, MySQL, SQL Server), and data processing frameworks like PySpark or Spark.
Experience with at least one major cloud platform (AWS, Azure, or GCP) and its data/AI services.
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, AI, ML, or related technical field, or equivalent practical experience.
Experience working in fast-paced, global, cross-functional teams handling complex AI/ML and data engineering projects, especially in private equity or consulting contexts.
Strong expertise integrating and developing AI/LLM applications including RAG, vector search, embeddings, and familiarity with AI/LLM frameworks such as LangChain or Semantic Kernel.
Operates with a high degree of ownership and pragmatism, balancing quality, business needs, and delivery timelines while proactively addressing data and AI system issues.