- Solutions Architect/Engineer - Data & Agentic AI Solution

Artech L.L.C.; Dadri; On-site; INR 3,500,000 - 6,500,000; Full time

Job summary

Artech L.L.C. is seeking a highly technical AI Solutions Architect/Engineer with deep expertise in Data Platforms, Cloud Architecture, AI/ML Operations, and Agentic AI solution implementation.

The role emphasizes architecture design, rapid prototyping, and engagement with sales and delivery teams to craft scalable AI-driven data platforms. The candidate will collaborate with Solutions Architects, Proposal Managers, Delivery Teams, Sellers and Client Stakeholders to deliver forward deployment

Qualifications

• 13+ years of experience in Data Engineering, Cloud Architecture, Analytics, AI/ML, or Solution Architecture.
• Strong hands-on implementation experience in enterprise data platforms.
• Experience supporting client-facing consulting or pre-sales activities.

Responsibilities

• Partner directly with clients, sellers, and practice leadership to understand business challenges and technical requirements.
• Rapidly design and prototype scalable data and AI solutions.
• Translate client requirements into actionable architecture and delivery plans.
• Conduct technical discovery workshops and architecture whiteboarding sessions.
• Support client demonstrations, proofs-of-concept, pilot implementations, and modernization initiatives.
• Design cloud-native and AI-enabled data platforms.
• Troubleshoot and resolve architecture, integration, and deployment issues.

Skills

Databricks

Snowflake

Microsoft Fabric

Data Lakes / Lakehouse

Kafka / Event Streaming

LangChain / LangGraph

AI orchestration frameworks

LLM integration patterns

Python

SQL

PySpark

APIs & Microservices

Terraform

RBAC / IAM

Metadata management

Data governance frameworks

Enterprise security and compliance

Job description

We are seeking a highly technical, Agentic AI Solutions Architect/Engineer with deep expertise in Data Platforms, Cloud Architecture, AI/ML Operations, and Agentic AI solution implementation.

This role is not a traditional delivery-only Data Architect role.

The ideal candidate must be able to:

• Operate effectively in highly ambiguous environments
• Rapidly transform incomplete client requirements into structured technical solutions
• Build assumptions, workload models, LOE estimates, and delivery approaches
• Interface directly with Sales, Clients, Delivery, and Practice Leadership
• Prototype and operationalize modern data and AI architectures
• Support pre-sales, RFP responses, and technical solutioning
• Create reusable accelerators, frameworks, and implementation patterns

The role combines:

• Technical Consulting
• Client Engagement
• Prototype & Accelerator Development

This individual will work closely with:

• Solutions Architects
• Proposal Managers
• Delivery Teams
• Sellers and Client Stakeholders

Key Responsibilities

1. Forward Deployment Engineering & Client Solutioning

• Partner directly with clients, sellers, and practice leadership to understand business challenges and technical requirements
• Rapidly design and prototype scalable data and AI solutions
• Translate incomplete or evolving client requirements into actionable architecture and delivery plans
• Conduct technical discovery workshops and architecture whiteboarding sessions
• Support client demonstrations, proof-of-concepts, pilot implementations, and modernization initiatives
• Design and operationalize cloud-native and AI-enabled data platforms
• Troubleshoot and resolve architecture, integration, and deployment issues during solution development
• Snowflake
• Databricks
• Microsoft Fabric
• AWS Data Services
• Lakehouse / Medallion Architectures
• Data Mesh Patterns
• Design scalable ingestion, transformation, orchestration, and analytics frameworks
• Implement metadata-driven and self-healing pipeline concepts
• Design lineage, governance, and cataloging approaches using:
• Microsoft Purview
• Collibra
• Alation
• Snowflake Horizon
• Design secure, compliant architectures aligned with enterprise governance requirements
• RAG systems
• Agentic AI frameworks
• LLM integrations
• Semantic search
• AI orchestration frameworks

3. Agentic AI & AI Enablement

• Build and operationalize AI workflows using:
• OpenAI
• Gemini
• LangChain
• LangGraph
• Semantic Kernel
• AI agents
• Retrieval Augmented Generation (RAG)
• AI orchestration pipelines
• Autonomous workflows
• Participate in AI governance, observability, prompt engineering, and model evaluation activities
• Develop reusable AI accelerators and implementation templates
• Participate in RFP, RFI, and proposal response development
• Build:
• Assumptions frameworks
• Delivery approaches
• Pricing support inputs
• Collaborate with:
• Proposal Management
• Delivery Leadership
• Translate technical architectures into delivery staffing and operational models
• Support architecture reviews, proposal reviews, and red team reviews
• Participate in technical orals and client solution presentations

5. Practice Development & IP Creation

• Build reusable:
• Accelerators
• Governance models
• Technical playbooks
• AI implementation patterns
• Support development of the Data Intelligence Center of Excellence (COE)
• Contribute to GTM strategy and packaged service offerings
• Collaborate with Marketing on technical collateral and case studies

Required/Desired Technical Skills

• Databricks
• Snowflake
• Microsoft Fabric
• Data Lakes / Lakehouse architectures
• Kafka / Event Streaming

AI / ML / Agentic AI

• LangChain / LangGraph
• AI orchestration frameworks
• LLM integration patterns
• AI observability concepts
• Python
• SQL
• PySpark
• APIs & Microservices
• Terraform

Governance & Security

• RBAC / IAM
• Metadata management
• Data governance frameworks
• Enterprise security and compliance standards

Required Experience

• 13+ years of experience in Data Engineering, Cloud Architecture, Analytics, AI/ML, or Solution Architecture
• Strong hands-on implementation experience in enterprise data platforms
• Experience supporting client-facing consulting or pre-sales activities
• Experience building technical proposals, architecture diagrams, and implementation approaches
• Experience of working directly with business stakeholders and enterprise clients
• Experience operating in ambiguous, fast-moving consulting environments

Experience estimating projects and supporting staffing / LOE models