GenAI Architect & Data Scientist

About Spar Information Systems

Spar Information Systems, our client, is actively seeking a GenAI Architect & Data Scientist. This pivotal role is being advertised through Dice, the premier career destination for tech experts.

GenAI Architect & Data Scientist

We are seeking a GenAI Architect & Data Scientist to design and build intelligent, scalable AI systems with a strong focus on Natural Language Processing (NLP) and Generative AI driven conversational experiences. The ideal candidate will combine deep data science expertise with architectural thinking to deliver intent driven, context aware, and production ready AI solutions across cloud platforms. This role involves understanding user intent, designing dialogue flows, applying generative AI, and operationalizing models using MLOps best practices to automate and enhance complex language interactions across multiple channels.

This role requires 12+ years of hands-on and architectural experience in large-scale enterprise environments.

Key Responsibilities

• GenAI & NLP Solution Design: Design and architect Generative AI solutions leveraging large language models (LLMs). Build and optimize NLP pipelines for intent detection, entity extraction, sentiment analysis, summarization, and conversational understanding. Apply generative AI techniques to analyze, interpret, and automate complex language-based interactions. Design scalable AI architectures that support multichannel conversational platforms (web, chat, voice, APIs).
• Intent Design & Conversational AI: Act as an Intent Designer by analyzing user behavior and business requirements to define clear intent hierarchies. Design accurate dialogue flows, fallback strategies, and contextual conversation handling. Improve conversational accuracy using prompt engineering, fine-tuning, and retrieval augmented generation (RAG) patterns. Ensure conversational experiences are natural, explainable, and aligned with business goals.
• Data Science & Model Development: Develop, train, evaluate, and optimize machine learning and deep learning models for NLP use cases. Work with structured and unstructured data to derive insights and improve AI model performance. Perform feature engineering, experimentation, and model validation.
• MLOps & Production Readiness: Implement MLOps pipelines for model versioning, deployment, monitoring, and retraining. Ensure AI models are production-ready with observability, performance tracking, and governance controls. Collaborate with DevOps and platform teams to integrate AI solutions into enterprise systems.
• Cloud AI & Architecture: Design and deploy AI solutions using Cloud AI platforms (AWS, Azure, or Google Cloud Platform). Leverage managed AI/ML services for model training, inference, and orchestration. Ensure scalability, security, cost optimization, and compliance in cloud-based AI architectures.
• Collaboration & Leadership: Collaborate with product managers, engineers, UX designers, and stakeholders. Provide architectural guidance and technical leadership for GenAI initiatives. Mentor data scientists and engineers on NLP, GenAI, and MLOps best practices.

Required Skills

Core Technical Skills:

• Strong expertise in Natural Language Processing (NLP).
• Hands-on experience with Generative AI and Large Language Models (LLMs).
• Proven experience in Intent Design and conversational AI systems.
• Strong background in Data Science and Machine Learning.
• Experience implementing MLOps pipelines (CI/CD for ML, monitoring, governance).
• Proficiency in Python for data science and AI development.

Cloud & Platforms:

• Hands-on experience with Cloud AI platforms (AWS, Azure, or Google Cloud Platform).
• Experience deploying AI models at scale in cloud environments.

Conversational AI:

• Experience building intelligent conversational experiences using:
  • Intent classification
  • Dialogue flow design
  • Context management
  • Prompt engineering
  • RAG based architectures

Good to Have (Preferred Skills)

• Experience with vector databases and semantic search.
• Knowledge of AI ethics, bias mitigation.

Key skills/competency

• Generative AI
• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Data Science
• Machine Learning
• MLOps
• Cloud AI Platforms (AWS, Azure, GCP)
• Conversational AI
• Prompt Engineering
• Retrieval Augmented Generation (RAG)