Principal Architect – Data & AI/ML Practice

Job Description

Principal Architect – Data & AI/ML Practice : Hands-On | Individual Contributor + Technical Leadership

Function : Data, Analytics & AI/ML Practice

Level : Principal / Senior Staff

Location : Hyderabad

Employment : Full-Time

About CriticalRiver

CriticalRiver is a technology services firm delivering digital transformation, cloud, data, analytics, and AI/ML solutions to enterprises globally. Our Data, Analytics & AI/ML practice is a fast-growing centre of excellence that spans classical data engineering, modern lakehouse architectures, advanced analytics, and Generative AI – helping clients move from raw data to decision intelligence at scale.

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 tha…