Job Description

Overview:

Focus: 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:

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.

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.