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Tier-1 brand, mid-level experience band, and metro location increase applicant competition.
Specialized GenAI, RAG, and vector-database skills make cross-industry transfers limited and domain-specific.
Explicit 4-5 year requirement plus specific LLM, cloud, and data-stack skills make shortlisting strict.
Build and maintain data models, ETL/ELT pipelines, and AI/LLM-enabled applications to support analytics and private equity diligence workflows.
Develop, integrate, and optimize AI/LLM capabilities including Retrieval-Augmented Generation, semantic search, and vector search to improve relevance and performance of AI applications.
Collaborate across global teams to translate business requirements into scalable data engineering and AI solutions while ensuring data quality, security, and adherence to governance standards.
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, AI, ML, or a related technical field (or equivalent experience).
4-5 years of professional experience in data engineering, AI/ML engineering, or software engineering building data-intensive or AI-enabled applications.
Proficiency in Python programming, SQL, and experience with data processing frameworks like PySpark or Spark.
Experience with cloud platforms (AWS, Azure, or GCP) and familiarity with Generative AI/LLM concepts including RAG application development.
Experienced in designing and implementing complex data pipelines and AI-enabled applications in fast-paced, global, cross-functional environments.
Comfortable with advanced AI/LLM technologies such as vector databases, embeddings, prompt engineering, and frameworks like LangChain or LlamaIndex.
Skilled at collaborating with diverse teams, explaining technical concepts to non-technical stakeholders, and handling ambiguous requirements.