Senior Solution Architect - Data & AI - Presales

We are seeking a highly skilled Modern Data & AI professional with hands-on experience across cloud data platforms, advanced data engineering, AI/ML frameworks, GenAI ecosystems, and presales activities. The candidate will design, build, and present scalable data and AI solutions to clients, supporting enterprise digital transformation initiatives.

Key Responsibilities

Presales & Solutioning

• Engage with clients to understand business requirements and translate them into technical solutions.

• Conduct solution workshops, technical demos, and POCs showcasing capabilities in Modern Data Platforms, AI/ML, and GenAI.

• Support proposal development, RFP responses, and commercial discussions with stakeholders.

• Provide guidance on architecture, feasibility, and technology adoption strategies.

Modern Data Platforms

• Design and implement solutions on Databricks, Snowflake, Cloudera CDP, Microsoft Fabric, and Google BigQuery.

• Develop Lakehouse and distributed data systems with high performance and reliability.

Data Engineering

• Build robust data pipelines using Spark, Airflow, Delta Lake, Kafka, and DBT.

• Ensure data quality, observability, and reliability in production workflows.

Data Governance & Catalog

• Implement enterprise governance frameworks leveraging Informatica, Microsoft Purview, Collibra, Unity Catalog, and Alation.

• Establish policies for metadata management, lineage, data quality, and compliance.

MLOps & AI Engineering

• Operationalize ML pipelines using MLflow, Kubeflow, Amazon SageMaker, and Azure ML.

• Automate model training, deployment, registry, and monitoring within scalable CI/CD workflows.

ML & AI Algorithms

• Develop AI models including Neural Networks, Classical ML algorithms, Transformers, NLP, Computer Vision, and Recommendation Systems.

• Optimize model performance for enterprise-grade use cases.

GenAI Platforms

• Build solutions using Azure OpenAI / Foundry, AWS Bedrock, and Vertex AI.

• Implement prompt engineering, fine-tuning, RAG pipelines, and AI agent workflows.

Agentic AI Development

• Design autonomous agent systems using LangChain, CrewAI, AutoGen, LlamaIndex, MCP, and A2A.

• Integrate multi-agent orchestration and tool calling capabilities for enterprise automation.

RAG & Vector Database Development

• Implement Retrieval-Augmented Generation (RAG) systems using Pinecone, Weaviate, Redis Vector, and Milvus.

• Build semantic search, embedding pipelines, and vector store integrations.

Data Architecture

• Define and implement enterprise architectures such as Data Mesh, Data Fabric, and Lakehouse.

• Drive architectural governance, best practices, and capability roadmaps.

Industry Use Cases

• Deliver solutions for Fraud Detection, Customer 360, Document AI, Predictive Analytics, and other industry-specific AI initiatives.

Requirements

• Hands-on engineering experience in modern cloud data platforms and AI/ML frameworks.

• Experience delivering end-to-end Data & AI solutions in production.

• Proven presales experience: solution design, demos, POCs, RFP/RFI responses.

• Strong understanding of distributed systems, MLOps, GenAI, and enterprise data governance.

• Ability to communicate with senior stakeholders and translate business problems into scalable technical solutions.