Data Architect

Key Responsibilities

Architecture & Solution Design

• Architect end‑to‑end data platforms, AI/ML systems, and GenAI solutions across cloud environments.
• Design scalable data pipelines, lakehouses, warehouses, and real‑time processing architectures.
• Define reference architectures, best practices, and reusable frameworks for Data & AI delivery.
• Ensure solutions meet performance, security, governance, and compliance requirements.

Technical Leadership

• Provide architectural oversight to engineering teams across data engineering, ML, and GenAI projects.
• Review solution designs, code, and deployment pipelines to ensure technical quality.
• Guide teams on modern data stacks, cloud-native patterns, and AI/ML engineering practices.
• Mentor engineers and analysts to strengthen Data & AI capabilities.

Client Engagement & Presales

• Work with sales and presales to craft solution architectures, proposals, and technical presentations.
• Engage with client architects, product owners, and C‑suite stakeholders to understand business needs.
• Translate business challenges into scalable, outcome‑driven Data & AI solutions.
• Support estimation, scoping, and technical risk assessment.

Delivery Excellence

• Oversee implementation of data platforms, ML models, and GenAI workflows.
• Ensure adherence to architectural standards, data quality, and engineering best practices.
• Drive performance optimization, cost efficiency, and reliability across deployed systems.
• Establish CI/CD, MLOps, and LLMOps pipelines for production‑grade deployments.

Innovation & Thought Leadership

• Evaluate emerging technologies across AI/ML, GenAI, LLMOps, and cloud data platforms.
• Build accelerators, reusable components, and architectural blueprints.
• Contribute to internal knowledge sharing, blogs, whitepapers, and tech talks.

Technical Skills & Expertis

eData Engineering & Analytic

• sStrong expertise in SQL, ETL/ELT, data modeling, and pipeline orchestration
• .Experience with lakehouse and warehouse platforms (Databricks, Snowflake, Redshift, BigQuery)
• .Hands‑on with PySpark, Python, Scala, and distributed data processing

.AI/M

• LExperience in feature engineering, model development, evaluation, and optimization
• .Familiarity with ML frameworks: TensorFlow, PyTorch, Scikit‑learn, XGBoost, LightGBM
• .Applied ML experience in forecasting, NLP, classification, clustering, and anomaly detection

.GenAI & LLM Ecosystem

• sExperience designing RAG architectures and LLM‑powered applications
• .Knowledge of vector databases (Pinecone, Qdrant, FAISS, Chroma)
• .Familiarity with multi‑agent frameworks (LangGraph, CrewAI, AutoGen)
• .Strong understanding of embeddings, prompt engineering, fine‑tuning, and document intelligence

.Cloud Platform

• sHands‑on experience with at least one major cloud
• :Azure: Azure AI, OpenAI, Data Factory, Synapse, Databrick
• sAWS: Bedrock, Glue, Athena, S3, SageMake
• rGCP: Vertex AI, BigQuery, Dataflo

wMLOps & LLMOp

• sCI/CD for ML and GenAI pipelines
• .Model deployment using Docker, Kubernetes, and serverless patterns
• .Monitoring for drift, accuracy, hallucination checks, and model lifecycle management

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Requiremen

• tsBachelor’s degree in Engineering, Computer Science, or related fiel
• d.10–15 years of experience in Data Engineering, AI/ML, or Cloud Data Architectur
• e.Proven experience architecting enterprise‑scale data and AI solution
• s.Strong understanding of cloud-native architectures and modern data stack
• s.Experience working with cross‑functional teams in a matrix environmen
• t.Excellent communication and stakeholder engagement skill
• s.Ability to translate business needs into robust technical architecture
• s.Strong problem‑solving mindset with a focus on scalability, security, and performanc