Role Overview

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.

Data Engineering & Integration

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.

Design MLOps platforms using MLflow, Kubeflow, SageMaker, Azure ML, or Vertex AI.

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.

Data Strategy & Governance

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.

Pre-Sales & Practice Development

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.

GenAI & LLM: LangChain / LlamaIndex, OpenAI / Azure OpenAI / Bedrock / Gemini APIs, vector DBs (Pinecone, Weaviate, pgvector).

Cloud: AWS, Azure, or GCP — certified preferred; multi-cloud experience strongly valued.

Infrastructure-as-Code: Terraform, Bicep, or CloudFormation.

Containerisation & orchestration: Docker, Kubernetes.

Snowflake and Databricks (Must Have) Architecture & Design

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.

Soft Skills & Leadership

Excellent executive-level communication — ability to present complex technical topics clearly to C-suite and non-technical stakeholders.

Robust 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.

📌 Practice Architect – Data & AI/ML - Snowflake and Databricks (Hyderabad)
🏢 CriticalRiver
📍 Hyderabad