#EG Data & AI Architect

About the Company

Responsibilities

• ● Design Data & AI Architectures Architect cloud-native data platforms and pipelines for ingestion, transformation, analytics, and visualization.
• ● Design data lakehouse and real-time streaming architectures with strong governance and performance standards.
• ● Ensure the architecture supports scalability, resilience, and security across cloud and hybrid environments.
• ● Lead the design of AI and agentic architectures that combine data pipelines, large language models (LLMs), and reasoning engines.
• ● Use frameworks such as LangChain, LlamaIndex, and Retrieval-Augmented Generation (RAG) to enable contextual, autonomous AI applications.
• ● Leverage hyperscaler ecosystems including Google Gemini, Agentspace, AWS Bedrock, and Azure AI Studio to operationalize next-generation AI systems.
• ● Oversee technical design and ensure end-to-end architecture quality across delivery teams.
• ● Provide technical leadership on solution integration, cost optimization, and compliance.
• ● Collaborate with software, cloud, and data engineers to embed AI capabilities seamlessly into enterprise platforms.
• ● Develop and evangelize AI-first and agentic design principles across NCS and with clients.
• ● Create reusable blueprints, accelerators, and reference architectures to accelerate adoption.
• ● Mentor teams to uplift engineering and AI capabilities within NCS.
• ● Partner with clients to translate business needs into scalable data and AI solutions.
• ● Collaborate with leading cloud and AI technology partners to co-develop new industry offerings.
• ● Represent NCS in technology forums and client showcases as a thought leader in data and AI innovation.

Requirements

• ● Bachelor's or Master's degree in Computer Science, Data Engineering, Artificial Intelligence, or related fields.
• ● 8-12 years of experience in technology, including at least 5 years in data, AI, or solution architecture roles.
• ● Strong hands-on experience in one or more cloud ecosystems (Google Cloud, AWS, Azure).
• ● Expertise in data engineering frameworks (Databricks, Spark, Kafka, Airflow, DBT) and modern AI architectures (LLM, RAG, agentic systems).
• ● Familiarity with vector databases (FAISS, Qdrant, Weaviate, Chroma) and MLOps / AIOps pipelines.
• ● Excellent stakeholder communication skills - able to articulate technical strategy in business terms.
• ● Google Professional Data Engineer / Machine Learning Engineer certification is preferred.
• ● AWS Certified Machine Learning or Data Analytics Specialty certification is preferred.
• ● Microsoft Azure AI Engineer Associate certification is preferred.
• ● Databricks Certified Data Architect / Machine Learning Professional certification is preferred.

Benefits

• ● Lead high-impact AI management consulting programs for major enterprises and public sector clients.
• ● Shape enterprise strategies and governance frameworks that drive real transformation.
• ● Work with a talented, multi-disciplinary team.