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.

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.

Focus: Systems Integration, Data Engineering & Complex Logic
Experience: 5–8 Years

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.