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Gen AI Architect

Job in 110006, Delhi, Delhi, India
Listing for: GoML
Full Time position
Listed on 2026-02-27
Job specializations:
  • IT/Tech
    AI Engineer, Data Engineer, Data Science Manager, Machine Learning/ ML Engineer
Job Description & How to Apply Below
As AI Architect you will Define enterprise data and AI strategies, designing scalable architectures for data warehouses, lakes, and intelligent analytical platforms. Lead modernization initiatives transforming legacy systems into cloud-native, AI-ready solutions. Establish data governance, MLOps frameworks, and integration patterns for traditional analytics and generative AI applications across diverse client engagements.

You Will
Design end-to-end architectures spanning data platforms, analytics, and AI/ML systems from ingestion through model deployment
Develop roadmaps for legacy modernization, cloud migration, and AI adoption including LLM integration strategies
Define data and ML modeling standards, feature engineering patterns, and model governance frameworks
Architect GenAI solutions including RAG systems, agent frameworks, and enterprise knowledge bases with proper security controls
Evaluate technology stacks for data pipelines, ML platforms, vector databases, and LLM orchestration tools
Establish governance frameworks covering data quality, model monitoring, AI ethics, and responsible AI practices
Create architectural blueprints, reference architectures, and technical specifications for hybrid data-AI solutions
Provide thought leadership on AI/ML trends, emerging technologies, and industry convergence of analytics and intelligence

Who are you
10+ years in data/analytics with 5+ years architecting solutions spanning traditional BI and AI/ML platforms
Deep expertise in data warehousing, data lakes, and modern lakehouse architectures combined with MLOps and AI infrastructure
Strong knowledge of on-premise and cloud platforms (AWS, Azure, Snowflake, Databricks) plus AI services (Bedrock, Azure OpenAI)
Experience designing LLM applications, RAG architectures, vector search systems, and agentic AI workflows
Understanding of ML lifecycle management, feature stores, model registries, and deployment patterns
Proven track record architecting regulatory reporting, financial services, and AI-augmented analytical solutions
Knowledge of AI governance, model explainability, bias detection, and responsible AI frameworks
Exceptional communicator bridging business strategy, data engineering, and AI innovation for technical and executive audiences
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