Senior data architect

Key Responsibilities
Architecture & Solution Design
- Architect end‑to‑end data platforms, AI/ML systems, and Gen AI 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 Gen AI 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 Gen AI 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, Gen AI, LLMOps, and cloud data platforms.
- Build accelerators, reusable components, and architectural blueprints.
- Contribute to internal knowledge sharing, blogs, whitepapers, and tech talks.
Technical Skills & Expertise
Data Engineering & Analytics
- Strong expertise in SQL, ETL/ELT, data modeling, and pipeline orchestration.
- Experience with lakehouse and warehouse platforms (Databricks, Snowflake, Redshift, Big Query).
- Hands‑on with Py Spark, Python, Scala, and distributed data processing.
AI/ML
- Experience in feature engineering, model development, evaluation, and optimization.
- Familiarity with ML frameworks: Tensor Flow, Py Torch, Scikit‑learn, XGBoost, Light GBM.
- Applied ML experience in forecasting, NLP, classification, clustering, and anomaly detection.
Gen AI & LLM Ecosystems
- Experience designing RAG architectures and LLM‑powered applications.
- Knowledge of vector databases (Pinecone, Qdrant, FAISS, Chroma).
- Familiarity with multi‑agent frameworks (Lang Graph, Crew AI, Auto Gen).
- Strong understanding of embeddings, prompt engineering, fine‑tuning, and document intelligence.
Cloud Platforms
- Hands‑on experience with at least one major cloud:
- Azure: Azure AI, Open AI, Data Factory, Synapse, Databricks
- AWS: Bedrock, Glue, Athena, S3, Sage Maker
- GCP: Vertex AI, Big Query, Dataflow
MLOps & LLMOps
- CI/CD for ML and Gen AI pipelines.
- Model deployment using Docker, Kubernetes, and serverless patterns.
- Monitoring for drift, accuracy, hallucination checks, and model lifecycle management.
Requirements
- Bachelor’s degree in Engineering, Computer Science, or related field.
- 10–15 years of experience in Data Engineering, AI/ML, or Cloud Data Architecture.
- Proven experience architecting enterprise‑scale data and AI solutions.
- Strong understanding of cloud-native architectures and modern data stacks.
- Experience working with cross‑functional teams in a matrix environment.
- Excellent communication and stakeholder engagement skills.
- Ability to translate business needs into robust technical architectures.
- Strong problem‑solving mindset with a focus on scalability, security, and performance.