Job description
Overview
Systems Integration, Data Engineering & Complex Logic Experience: 5–8 Years Role Summary: You are a hands-on architect who understands how to integrate Generative AI into complex, existing enterprise ecosystems. You possess strong Data Engineering skills and a grasp of Classical Machine Learning. You integrate GenAI into enterprise ecosystems involving legacy systems, complex data estates, and multi-step data pipelines.
Responsibilities
Complex System Integration: Integrate LLM solutions with legacy systems (ERPs, CRMs) and multiple data sources. Design systems that utilize Graph Databases or complex SQL queries alongside Vector Stores. Data Engineering: Build robust data pipelines (ETL/ELT) for GenAI strategies. Understand MapReduce principles and data structure analysis.
Hybrid AI Approach: Know when to use Classical Machine Learning (Regression, Classification) versus Generative AI. Data Strategy: Formulate data strategies for clients, selecting the right database types (RDB, NoSQL, Graph, In-memory Cache) for the specific use case. data governance: Implement lineage, PII handling, access control.
Architecture: Build event-driven architectures and multi-agent orchestration. Testing: Strategize and conduct integration testing, regression testing, QA. Customer Guidance: Provide architectural guidance and educate customers Requirements Technical Requirements: Data: Pandas, SQL optimization, Graph DBs (Neo4j), Vector Database internals.
Integration: Event-driven architecture, Enterprise Service Bus patterns, Multi-agent orchestration. ML Knowledge: Understanding of statistics, classical ML algorithms, and data structure analysis. Security: IAM, governance, audit trails. Soft Skills: Strong stakeholder management across engineering, data, and business.
Ability to simplify complex technical concepts for non‑technical leaders. Solution‑oriented thinking under ambiguity. Mentorship and coaching of Lead and Junior FDEs. High empathy and customer‑centric design approach.
Key Responsibilities
Complex System Integration: Integrate LLM solutions with legacy systems (ERPs, CRMs) and multiple data sources. Design systems that utilize Graph Databases or complex SQL queries alongside Vector Stores. Data Engineering: Build robust data pipelines (ETL/ELT) for GenAI strategies. Understand MapReduce principles and data structure analysis.
Hybrid AI Approach: Know when to use Classical Machine Learning (Regression, Classification) versus Generative AI. Data Strategy: Formulate data strategies for clients, selecting the right database types (RDB, NoSQL, Graph, In-memory Cache) for the specific use case. data governance: Implement lineage, PII handling, access control.
Architecture: Build event-driven architectures and multi-agent orchestration. Testing: Strategize and conduct integration testing, regression testing, QA. Customer Guidance: Provide architectural guidance and educate customers Technical Requirements: Data: Pandas, SQL optimization, Graph DBs (Neo4j), Vector Database internals.
Integration: Event-driven architecture, Enterprise Service Bus patterns, Multi-agent orchestration. ML Knowledge: Understanding of statistics, classical ML algorithms, and data structure analysis. Security: IAM, governance, audit trails. Soft Skills: Strong stakeholder management across engineering, data, and business.
Ability to simplify complex technical concepts for non‑technical leaders. Solution‑oriented thinking under ambiguity. Mentorship and coaching of Lead and Junior FDEs. High empathy and customer‑centric design approach.