The primary focus of this role is on AI Architecture, with strong expertise in Graph Databases and AWS. The ideal candidate should be a strong individual contributor (IC), capable of directly interacting with clients and taking ownership of technical discussions. Hands-on coding is expected whenever required.
Key Responsibilities
Create and optimize data schemas tailored for machine learning and analytics use cases
Design and implement data pipelines involving:
Data ingestion (batch and streaming)
ETL processes
Workflow orchestration on AWS
Build, deploy, and maintain REST APIs for data access and integration
Define architecture patterns for AI/LLM-enabled search, retrieval, and analytics capabilities.
Integrate solutions with Microsoft Teams for notifications, workflows, or insights sharing
Collaborate with SAP functional and technical teams to support security, access, and analytics use cases
Ensure data quality, performance, and scalability across platforms
Define scalable and secure cloud-native architecture using AWS services and DevOps best practices.
Support AI response stabilization approaches including controlled retrieval, indexing strategies, contextual filtering, and workflow orchestration.
Design, develop, and manage graph data models, with a strong focus on Neo4j.
Required Skills & Experience
Strong understanding of data modeling and schema design for ML-driven applications
Experience designing and operating data pipelines using AWS services (e.g., ingestion, ETL, orchestration)
Good exposure to AWS Bedrock, AWS SageMaker, Bedrock Agents, MCP, LangChain/LangGraph, Python, FastAPI, FAISS, RAG Architecture.
Strong understanding of cloud-native architectures, microservices, and distributed systems.
Experience with AI/LLM ecosystems, semantic search, vector databases, embeddings, or Retrieval-Augmented Generation (RAG) concepts.
Hands-on experience with graph databases, preferably Neo4j
Experience in API development and deployment
Working knowledge of Microsoft Teams integrations
Ability to work closely with cross-functional teams and explain technical concepts clearly
Nice to Have
Exposure to security analytics or access-control-related analytics
Basic knowledge of SAP Security / SoD concepts
Prior experience working in SAP-driven enterprise environments
Exposure to DevOps, Infrastructure as Code (Terraform/CloudFormation), Docker, or Kubernetes.
AI Architectcure, AWS, AWS Bedrock, AWS SageMaker, Bedrock Agents, MCP, LangChain, LangGraph, Graph Databases, Python, FastAPI, FAISS, Retrieval Augmented Generation, REST API Development, API Deployment, Cloud Native Architecture, Microservices, Distributed Systems, Semantic Search, Vector Databases, Embeddings, Microsoft Teams Integration, Security Analytics, Access Control Analytics, Segregation Of Duties, DevOps, Infrastructure As Code, Terraform, CloudFormation, Docker, Kubernetes, Data Quality, Performance Optimization, Scalability, Cloud Security, AI Architecture, LLM Applications, Information Retrieval, Contextual Filtering, Indexing Strategies, Workflow Automation, Data Engineering, Analytics Engineering