Job Title: Principal Architect – Data & AI (AI-Ready Data)

Location: Bangalore / Mangalore – Hybrid

Type: Full-time

Why MResult?

Founded in 2004, MResult is a global digital solutions partner trusted by leading Fortune 500 companies in industries such as pharma & healthcare, retail, and BFSI. MResult’s expertise in data and analytics, data engineering, machine learning, AI, and automation helps companies streamline operations and unlock business value. As part of our team, you will collaborate with top minds in the industry to deliver cutting-edge solutions that solve real-world challenges.

Website: https://mresult.com/

LinkedIn: https://www.linkedin.com/company/mresult/

What We Offer

At MResult, you can leave your mark on projects at the world’s most recognized brands, access opportunities to grow and upskill, and do your best work with the flexibility of hybrid work models. Great work is rewarded, and leaders are nurtured from within.

Our values — Agility, Collaboration, Client Focus, Innovation, and Integrity — are woven into our culture, guiding every decision.

About the Role

We are seeking a highly experienced, hands-on Principal Architect – Data & AI to lead the design and implementation of modern, scalable, AI-ready enterprise data platforms, and to be the senior-most builder on the team and a technical partner to the BU lead.

You will drive the evolution of traditional data platforms into AI-native, intelligent, and autonomous data ecosystems, enabling trusted enterprise data for analytics, Generative AI, and AI agents. You will set technical direction across multiple products and client engagements, define the standards everyone builds against, and stay hands-on with the hard problems.

The ideal candidate brings deep expertise in Data Engineering, Cloud Data Platforms (AWS and Azure), Data Architecture, Agentic AI, and Data Governance, with strong hands-on experience in Snowflake and Databricks. This includes building intelligent data solutions using Snowflake Cortex AI, Cortex Agents, Databricks Mosaic AI, and Agent Bricks, and building LLM/agentic systems from primitives with rigorous evaluation.

Key Responsibilities

- Technical Direction & Org-Level Decisions

- Set technical direction across multiple platforms, products, and client engagements: architecture, standards, and the reusable patterns the team builds on.
- Make org-level calls: build vs. buy, primitives vs. framework, what becomes a shared internal capability, and what will not go into production without explicit client sign-off.
- Lead empirical technical investigations (failure-mode studies, reproducibility, cost/quality trade-offs) and turn them into defensible, client-facing points of view.

2. AI-Ready Data Architecture & Strategy
- Define and implement enterprise AI-ready data architectures across Snowflake, Databricks, and cloud ecosystems (AWS and Azure).
- Architect scalable Lakehouse, Data Warehouse, and Data Lake solutions, including multi-store data design across warehouse, streaming, and operational stores.
- Establish architectural standards for data quality, metadata management, lineage, governance, security, cost, and observability.
- Design semantic layers, business ontologies, and governed data foundations supporting RAG, enterprise knowledge retrieval, and agentic AI applications.

3. Agentic AI for Data Engineering
- Design, develop, and deploy AI agents that automate data engineering activities: ETL/ELT code generation, pipeline development and optimization, automated monitoring, anomaly detection, root-cause analysis, failure remediation, schema evolution, and metadata-driven orchestration.
- Build multi-agent workflows using LangGraph,



LangChain, CrewAI, or equivalent frameworks, and from primitives where frameworks add unnecessary abstraction.
- Integrate agents with enterprise data platforms, orchestration tools, APIs, and CI/CD pipelines, with human-in-the-loop controls and operational guardrails.
- Define evaluation and observability standards for LLM/agentic systems covering reproducibility, calibration, and cost.

4. AI Agents for Data Quality & Governance
- Architect AI-powered data quality and governance agents for automated profiling, quality rule generation, anomaly detection, root-cause analysis, and remediation recommendations.
- Develop intelligent solutions for metadata discovery, data classification, PII identification, lineage analysis, impact assessment, and policy compliance.
- Integrate agents with Unity Catalog, Snowflake Horizon Catalog, Collibra, Microsoft Purview, or equivalent governance platforms.

5. Snowflake Cortex AI & Agent Development
- Build enterprise AI solutions using Snowflake Cortex AI and Cortex Agents, leveraging Cortex Analyst for natural-language analytics, Cortex Search for semantic retrieval, and Cortex AI Functions for intelligent data processing.
- Build RAG applications, governed semantic models, and agents that interact with Snowflake pipelines, tasks, stored procedures, and enterprise services.
- Implement agent orchestration, security, evaluation, observability, and production deployment best practices.

6. Databricks Lakehouse & AI Agents
- Own data platform architecture on (Azure) Databricks: medallion architecture, Delta Lake, Unity Catalog governance, cluster policy design, pipeline scalability, job orchestration, and Terraform-based infrastructure provisioning.
- Design and implement AI agents using Databricks Mosaic AI, Agent Bricks, AI/BI Genie, Vector Search, and MLflow (including MLflow-based model lifecycle management).
- Integrate agents with Delta Lake, Unity Catalog, Lakeflow pipelines, and Databricks Workflows, with multi-agent orchestration, evaluation, monitoring, and scalable production deployment.

7. Technical Leadership & Solution Architecture
- Stay hands-on: write the hard parts, prototype risky ideas, and prove approaches with real code and real measurements before the team commits.
- Lead solution design, architecture reviews, proofs of concept, and production implementations for enterprise Data & AI transformation initiatives.
- Develop reusable AI accelerators, reference architectures, engineering frameworks, and best practices.
- Own security and reproducibility in regulated contexts.
- Collaborate with Data Engineering, AI/ML, Platform Engineering, Security, and Governance teams.
- Support RFP responses, technical proposals, solution estimations, and customer architecture discussions, and represent technical strategy to senior stakeholders and clients.
- Multiply the team: set the engineering bar and mentor architects, Senior SDEs, and SDEs.

Required Skills
- Systems Design & Python: Mastery of Python and systems design, with a track record of architecting production systems that others build on. Strong SQL, PySpark, and distributed data processing.
- Enterprise Data Platforms: Strong hands-on expertise in Snowflake, Databricks, Data Lakehouse, Data Warehousing, Data Modeling, ETL/ELT, streaming,



and multi-store data design.
- Databricks: Production experience with (Azure) Databricks architecture: Delta Lake, Unity Catalog, cluster policy design, job orchestration, Terraform provisioning, and Databricks AI (Mosaic AI, Agent Bricks, MLflow, AI/BI Genie, Vector Search).
- Snowflake AI: Hands-on experience with Snowflake Cortex AI, Cortex Agents, Cortex Analyst, Cortex Search, semantic models, and AI-powered data applications.
- Agentic & Generative AI: Proven ability to build AI agents, multi-agent systems, RAG applications, LLM tool calling, and agent orchestration, both from primitives and with LangChain, LangGraph, CrewAI, and MCP, and to evaluate them rigorously (reproducibility, calibration, cost).
- Data Quality & Governance: Experience with data quality frameworks, data observability, metadata management, lineage, cataloging, access controls, and governance platforms such as Unity Catalog, Snowflake Horizon Catalog, Collibra, and Microsoft Purview.
- Cloud & Engineering: Broad and deep cloud architecture across AWS and Azure (GCP a plus), covering distributed systems, security, and cost, along with Git, CI/CD, DevOps, infrastructure as code, and production monitoring.
- Architectural Judgment: Strong opinions, loosely held, grounded in first principles: can derive the trade-off rather than cite the trend, with deep skepticism of unnecessary abstraction.
- Influence & Communication: Influences without authority; sets standards, writes the specs and docs others follow, and translates technical work into client-facing strategy.

Preferred Qualifications
- Experience delivering production-grade AI agents for data engineering, data quality, and governance automation.
- Exposure to enterprise semantic layers, knowledge graphs, business ontologies, and AI-ready data foundations.
- Experience in regulated industries such as Pharma / Life Sciences, Healthcare, Banking, or Insurance (pharma experience strongly valued).
- Supply chain and inventory operations domain depth: demand planning processes, MRP logic, inventory parameters, and how planning decisions translate into data requirements, enough to engage supply chain stakeholders credibly without hand-holding.

Certifications Snowflake, Databricks, or Skilled-level cloud architecture certifications (AWS / Azure) are valued, but they are never a substitute for demonstrated depth.

Expected Outcomes

- Adoption of AI-ready enterprise data architectures and clear technical standards across products and client engagements.
- Production-grade AI agents that improve data engineering productivity, automate data quality and governance processes, and enhance platform reliability.
- Reusable accelerators and reference architectures on Snowflake and Databricks, enabling secure, scalable, and governed enterprise AI adoption.
- A stronger engineering team, with a higher engineering bar and growing Senior SDEs and SDEs.

Ideal Candidate Profile A hands-on Principal Architect combining deep enterprise data and cloud architecture expertise with practical Agentic AI implementation experience. The candidate can architect, build, and deploy AI agents on Snowflake and Databricks, reason from first principles about trade-offs, lead complex Data & AI transformation initiatives, and take intelligent data solutions from proof of concept to production, while representing that work credibly to senior clients. Manage, Master, and Maximize with MResult

MResult is an equal-opportunity employer committed to building an inclusive environment free of discrimination and harassment.

Take the next step in your career with MResult — where your ideas help shape the future.

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