Michelin Jobs

Solution Architect- Data and AI

Michelin

Solution Architect- Data and AI

Posted Yesterday

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In-Office

Pune, Mahārāshtra

Expert/Leader

In-Office

Pune, Mahārāshtra

Expert/Leader

Owns enterprise data and AI architecture, including Lakehouse designs, streaming ingestion, data modeling, ML deployment, security, governance, CI/CD, and MLOps. Optimizes Databricks and PySpark workloads, evaluates platform choices, productionizes AI solutions, and supports PII/GDPR compliance. Coaches cross-functional delivery teams, influences stakeholders, contributes to data policy, and performs hands-on ingestion, validation, repository design, and troubleshooting. Requires 10–15 years of experience and delivery of enterprise Lakehouse and production AI/ML solutions.

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Solution Architect- Data and AI

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Job Description:

Own architectural decisions (ADRs), reference blueprints, and target-state designs; arbitrate trade-offs on latency, cost, and scalability.

Architect end-to-end data and AI solutions: medallion Lakehouse (Bronze/Silver/Gold) on ADLS Gen2 + Delta Lake, streaming ingestion, ML deployment pipelines.

Optimize large-scale PySpark/Databricks workloads — partitioning, AQE, skew handling, Photon, cluster tuning.

Design data models across PostgreSQL, nowflake (Kimball, Data Vault 2.0, denormalized serving).

Productionize ML/DL models via Azure ML, AKS, Functions; expose FastAPI endpoints for batch and real-time inference.

Enforce security and compliance — Entra ID, Key Vault, PII, Unity Catalog / Purview, GDPR.

Build CI/CD and MLOps pipelines (Azure DevOps, Terraform/Bicep, MLflow); instrument observability.

Data Modeling Architecture/Design must also be done by Data and AI Solution Architect

This typically includes STAR SCHEMA Design, Normalization/Denormalization Data Patterns, etc.

Ability to evaluate and recommend which platform component would be best fit for given requirement.

Example: Decide between Postgres Db/Mongo Db, Decide between ODAP/One System Platform, etc.

Implementation of Continuous Architecture

Know how about BI domain + AI domains (NLP, GEN A, RAG, Forecasting, Computer Vision, Agentic AI, LLM) average or above average knowledge of concepts and internal technicalities.

Coach delivery teams, align with Data Science, Product, QA, and Platform leads, and contribute to the CDO's data policy.

Operate hands-on when needed — ingestion, cleansing, validation, repository design, root-cause analysis.

Must-Have

Advanced Python, PySpark, SQL

Expert in Databricks, Delta Lake, Spark Structured Streaming

Strong Azure stack: ADLS Gen2, ADF, Synapse, Azure ML, AKS, Event Hubs, Key Vault

Proven design of medallion Lakehouse architectures at TB–PB scale

Deep DB expertise: PostgreSQL, Synapse, Snowflake, ADLS

ML/DL fluency: scikit-learn, TensorFlow, PyTorch, MLflow

Hands-on PII/GDPR data handling and cloud security

CI/CD & MLOps: Azure DevOps or GitHub Actions, Terraform/Bicep, Docker, Kubernetes

Behavioural Competencies :

Communication Clarity

Produces clear, concise architecture documents, runbooks and presentation materials appropriate to the audience.

Continuous Learning & Curiosity

Keeping up with industry advances,trends and applies relevant innovations pragmatically.

Risk Awareness & Security Mindset

Recognizing operational, security, privacy and model-risk exposures and designs to mitigate them.

Problem-Solving & Complexity Management

Decomposes ambiguous, large-scale problems into manageable components and pragmatic solutions.

Cross-Functional Collaboration & Team Enablement

Works effectively across disciplines and empowers teams to deliver.

Stakeholder Management & Influence

Builds trust with business leaders, product owners, legal, security and engineering; influences decisions without direct authority.

Strategic Thinking & Business Acumen

Understands business drivers and shapes AI solutions that deliver measurable value and align with long-term strategy.

Experience

10–15 years in data architecture, data engineering/data science.

Demonstrated delivery of at least one enterprise Lakehouse and one production AI/ML solution.

Nice-to-Have

Azure OpenAI, AI Search, RAG, vector databases

IoT ingestion patterns

Data Mesh / federated governance experience

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