Presales AI Solution Architect

E Solutions; Dadri; On-site; INR 220,000 - 320,000; Full time

Job summary

E Solutions is seeking a technically strong Presales AI Solution Engineer/Architect in India to lead technical solutioning for AI, GenAI, and data/ML opportunities. You will work with Sales, Engineering, Delivery and Product teams to transform customer requirements into scalable architectures.

You will own end-to-end responses, design secure enterprise AI solutions, and define implementation roadmaps across AWS, Azure and GCP, while driving win probability through strong solution defense

Qualifications

• Strong hands-on/architectural knowledge of Generative AI & LLMs like OpenAI, Claude, Gemini, Llama.
• Experience with AI Agents, multi-agent systems, tool calling, MCP, LangGraph/LangChain.
• Deep understanding of RAG, embeddings, vector search, hybrid search and enterprise knowledge integration.
• Knowledge of ML lifecycle, model training, inference, evaluation and MLOps.
• Data engineering, data lakes/lakehouses, structured/unstructured data, ETL/ELT.
• Experience with vector databases such as Pinecone, Weaviate, Milvus, pgvector, and Elasticsearch/OpenSearch.
• Proficiency in cloud AI services: AWS Bedrock/SageMaker, Azure OpenAI/AI Foundry, GCP Vertex AI.
• Architectural fluency in APIs, microservices, event-driven design, Docker and Kubernetes.
• Security focus including IAM, RBAC, encryption, OAuth/OIDC, secrets management, audit logging, governance.
• Observability: monitoring, tracing, prompt/model monitoring and LLMOps.
• Exposure to Computer Vision, Synthetic Data, Robotics or Physical AI is a plus.

Responsibilities

• Own end-to-end technical response for AI/GenAI RFPs, RFIs and proposals.
• Analyze requirements and prepare architecture, compliance, assumptions and dependencies.
• Design enterprise AI solutions across LLMs, RAG, AI Agents, MCP and data platforms.
• Define solution architecture across AWS, Azure and GCP AI services.
• Recommend LLMs, embedding models, vector databases and orchestration frameworks.
• Design secure enterprise architectures including RBAC, IAM, encryption and data governance.
• Prepare diagrams, tech stack, sizing, effort estimates, BOM and roadmap.
• Define AI PoCs/MVPs and support workshops, presentations and defense sessions.
• Collaborate with delivery teams to validate feasibility and approach.
• Identify reusable accelerators and reference architectures to speed responses.

Skills

Generative AI

LLMs

Agentic AI

RAG

ML/MLops

Data platforms

Vector databases

Cloud platforms

Architecture

Security governance

Observability

Tools

OpenAI

Claude

Gemini

Llama

LangChain

Pinecone

Weaviate

Milvus

pgvector

Elasticsearch/OpenSearch

AWS Bedrock

SageMaker

Vertex AI

Docker

Kubernetes

Job description

We are looking for a technically strong Presales AI Solution Engineer or Architect who can independently lead technical solutioning and RFP/RFI responses for AI, Generative AI, Agentic AI and Data/ML opportunities.

The candidate will work closely with Sales, Engineering, Delivery and Product teams to understand customer requirements and convert them into scalable, secure and commercially viable AI solution architectures.

Key Responsibilities:-

• Own end-to-end technical response for AI/GenAI RFPs, RFIs and customer proposals.
• Analyze RFP requirements and prepare technical solution architecture, compliance matrix, assumptions, dependencies and technical response documentation.
• Design enterprise AI solutions covering LLMs, RAG, AI Agents, Agentic workflows, MCP, ML/MLOps and data platforms.
• Define solution architecture across AWS, Azure and GCP AI services.
• Recommend appropriate LLMs, embedding models, vector databases, orchestration frameworks and AI infrastructure.
• Design secure enterprise architectures covering RBAC, IAM, encryption, API security, multi-tenancy, data privacy and governance.
• Prepare architecture diagrams, technology stack, sizing, effort estimation, resource plan, BOM and implementation roadmap.
• Define AI PoCs/MVPs and support customer workshops, technical presentations and solution defense sessions.
• Work with delivery teams to validate solution feasibility, estimation and implementation approach.
• Identify reusable AI accelerators, frameworks and reference architectures to improve RFP response speed and win probability.

Required Technical Expertise

• Strong hands-on/architectural knowledge of:
  • Generative AI & LLMs: OpenAI, Claude, Gemini, Llama and open-source models
  • Agentic AI: AI Agents, multi-agent systems, tool calling, MCP, CrewAI, LangGraph/LangChain or equivalent frameworks
  • RAG: embeddings, vector search, chunking, reranking, hybrid search and enterprise knowledge integration
  • AI/ML: ML lifecycle, model training, inference, evaluation and MLOps
  • Data: data engineering, data lakes/lakehouses, structured/unstructured data and ETL/ELT
  • Vector Databases: Pinecone, Weaviate, Milvus, pgvector, Elasticsearch/OpenSearch or equivalent
  • Cloud: AWS Bedrock/SageMaker, Azure OpenAI/AI Foundry and GCP Vertex AI
  • Architecture: APIs, microservices, event-driven architecture, Docker and Kubernetes
  • Security: IAM, RBAC, OAuth/OIDC, secrets management, encryption, audit logging and AI governance
  • Observability: monitoring, tracing, prompt/model monitoring and LLMOps
• Exposure to Computer Vision, Synthetic Data, Robotics or Physical AI will be an advantage.

Presales Skills

• Strong experience responding to enterprise RFPs/RFIs.
• Ability to convert business requirements into detailed technical architectures.
• Strong technical proposal and solution-document writing skills.
• Experience preparing solution diagrams, effort estimates, implementation plans and technical presentations.
• Strong customer-facing communication and solution-defense capabilities.
• Ability to work under aggressive RFP timelines and independently drive technical closure.

Preferred Profile

• Previous experience as AI Solution Architect, GenAI Architect, Presales Architect or AI Technical SME.
• Experience supporting large enterprise or global customer pursuits.
• Good understanding of AI commercial models, cloud costing and solution estimation.
• Relevant AWS/Azure/GCP or AI/ML certifications would be preferred.
• Primary Objective: Build technically differentiated, secure and implementable AI solutions that strengthen our RFP responses and improve overall deal conversion.