Practice Architect – Data & AI/ML - Snowflake and Databricks

CriticalRiver Inc.; Hyderabad; On-site; INR 3,000,000 - 9,000,000; Full time

Job summary

CriticalRiver Inc. is seeking a Principal Architect, Data & AI/ML who can bridge enterprise data strategy with hands-on coding and architecture leadership. The role spans data engineering, ML engineering, and solution architecture across multi-cloud environments, owning decisions end-to-end and guiding delivery teams.

You will drive pre-sales pursuits, create reusable accelerators, and mentor delivery teams while championing governance and responsible AI across engagements.

Qualifications

• 15+ years in data engineering, analytics, or AI/ML roles, with 4+ years as architect.

Responsibilities

• Design and own end-to-end reference architectures for data platforms and AI/ML pipelines.
• Lead architecture reviews, PoCs, and technical due-diligence for client engagements.
• Define architectural principles, design patterns, and guardrails; contribute to internal IP and accelerators.
• Translate business requirements into scalable, cost-optimised blueprints.

Skills

Python

SQL

Scala

Data modeling

Executive communication

Consulting mindset

Mentoring

Architecture ownership

Hands-on coding

Tools

dbt

Airflow

Spark

Kafka

Fivetran

Glue

ADF

Terraform

Snowflake

Databricks

BigQuery

Synapse

Redshift

MLflow

Kubeflow

SageMaker

Vertex AI

Job description

We are looking for a Principal Architect, Data & AI/ML who is equally comfortable whiteboarding an enterprise data strategy and rolling up their sleeves to write production-grade code, design reference architectures, and mentor delivery teams. This is a hands-on leadership role that sits at the intersection of data engineering, machine learning engineering, and solution architecture.

You will serve as the technical anchor across client engagements — owning architecture decisions end-to-end, driving pre-sales pursuits, and building reusable accelerators that amplify the practice's delivery capability. The ideal candidate has deep expertise across the full data & AI/ML value chain — from data foundations to decision intelligence.

Key Responsibilities

Solution Architecture & Technical Leadership

• Design and own end-to-end reference architectures for data platforms, lakehouses, AI/ML pipelines, and GenAI products across multi-cloud environments (AWS, Azure, GCP).
• Lead architecture reviews, proof-of-concepts, and technical due-diligence for client engagements.
• Define architectural principles, design patterns, and guardrails for the practice; contribute to CriticalRiver's internal IP and accelerator library.
• Translate business requirements into scalable, cost-optimised, and secure technical blueprints.

Generative AI & LLM-Ops

• Architect and build enterprise-grade Generative AI solutions using large language models (LLMs), retrieval-augmented generation (RAG), vector databases, and AI agents.
• Design LLM-Ops pipelines covering fine-tuning, prompt engineering, evaluation harnesses, guardrails, and model observability.
• Enable "no-data" and "agents-on-data" patterns — embedding AI agents into structured data workflows and decision processes.
• Architect modern data integration pipelines: batch, micro-batch, and real-time; ELT/ETL on cloud-native platforms.
• Lead lakehouse and warehouse modernisation engagements — design migration patterns, medallion architectures, and data contract frameworks.
• Define data pipeline standards using tools such as dbt, Apache Airflow, AWS Glue, Azure Data Factory, and Fivetran.

Data Warehouse & Lakehouse

• Provide deep expertise on Snowflake, Databricks, and cloud-native warehouses (BigQuery, Synapse, Redshift).
• Design multi-hop storage architectures (Bronze/Silver/Gold), partition strategies, indexing, and query optimisation.
• Guide clients through platform selection, TCO analysis, and migration roadmaps.

Data Streaming & Edge Computing

• Architect streaming and event-driven platforms using Apache Spark Structured Streaming, Kafka, Flink, and cloud-native event services.
• Design edge-to-cloud IoT data pipelines — from device ingestion to real-time analytics and actionable insights.
• Define SLA/SLO frameworks for latency-sensitive workloads.

AI/ML Enablement & MLOps

• Oversee the full ML lifecycle: feature engineering, model development, training infrastructure, hyperparameter tuning, deployment, and drift monitoring.
• Champion responsible AI practices — fairness, explainability, bias detection, and model governance.

Data Science & Advanced Analytics

• Guide data science teams on forecasting, prescriptive analytics, and decision-intelligence models that connect directly to business outcomes.
• Architect feature stores, experiment tracking systems, and model registries.
• Evaluate and adopt emerging frameworks for causal inference, simulation, and reinforcement learning as applicable.

BI, Semantic Layer & Self-Service Analytics

• Design semantic layers, metrics frameworks, and governed BI architectures that enable self-service analytics at scale.
• Advise on BI platform selection and implementation: Power BI, Tableau, Looker, ThoughtSpot, and headless BI tools.
• Define data products and data mesh principles — domain ownership, data contracts, and discoverability.
• Co-develop data strategy, roadmaps, and operating models with client CDOs, CTOs, and data leadership.
• Design and implement data governance frameworks covering data quality, data lineage, cataloguing, master data management (MDM), and privacy/compliance.
• Champion data literacy and centre-of-excellence models within client organisations.
• Support business development — respond to RFPs, present technical architecture in client pitches, and estimate delivery effort.
• Publish thought leadership: blogs, white papers, reference architectures, and conference talks.
• Mentor senior engineers and architects; drive the internal CoE agenda across upskilling and certification.

Required Qualifications & Skills

Experience

• 15+ years in data engineering, analytics, or AI/ML roles, with at least 4 years in a principal or lead architect capacity.
• Proven track record of delivering large-scale data platforms and AI/ML solutions in complex enterprise environments.
• Hands-on coding is mandatory — architecture without execution is not sufficient for this role.

Core Technical Proficiency (hands-on expected)

• Languages: Python, SQL, Scala (Must Have)
• Data platforms: Snowflake, Databricks, BigQuery, Synapse, or Redshift — at least two in depth.
• Data integration: dbt, Airflow, Spark, Kafka, ADF, Glue, Fivetran, or equivalent.
• ML frameworks: scikit-learn, XGBoost, PyTorch, TensorFlow; MLOps tools: MLflow, Kubeflow, SageMaker, or Vertex AI.
• Cloud: AWS, Azure, or GCP — certified preferred; multi-cloud experience strongly valued.
• Infrastructure-as-Code: Terraform, Bicep, or CloudFormation.
• Snowflake and Databricks (Must Have)
• Strong command of data modelling (dimensional, Data Vault 2.0, entity-centric), API design, and microservices patterns.
• Experience with data mesh, data fabric, and event-driven architecture patterns.
• Ability to produce detailed architecture documents, C4 diagrams, and ADRs independently.
• Excellent executive-level communication — ability to present complex technical topics clearly to C-suite and non-technical stakeholders.
• Strong consulting mindset: structured problem-solving, rapid context switching, and client-facing delivery confidence.
• Self-starter with the ability to work in ambiguous, fast-paced environments.

Good to Have

• Relevant cloud and data certifications: AWS Solutions Architect Professional, Azure Data Engineer / AI Engineer, GCP Professional Data Engineer, Databricks Certified Associate/Professional, Snowflake SnowPro Core.
• Contribution to open-source data or ML projects.
• Experience in regulated industries (BFSI, Healthcare, Retail) with data privacy compliance (GDPR, HIPAA).
• Exposure to graph databases, time-series platforms, or geospatial analytics.
• Prior consulting or Big-4 technology advisory background.

What We Offer

• Opportunity to shape the technical direction of a rapidly growing Data & AI/ML practice.
• Exposure to cutting-edge client challenges across industries and geographies.
• Access to the latest cloud, data, and GenAI platforms and tooling.
• Competitive compensation with performance-linked incentives.
• Sponsored certifications and continuous learning budget.
• Collaborative, high-trust culture with strong engineering values.