Solution Architect- Data and AI

Michelin; Maharashtra; On-site; INR 4,200,000 - 6,400,000; Full time

Job summary

Michelin in Maharashtra, India seeks an experienced Data and AI Solution Architect to own end‑to‑end architectural decisions for Lakehouse platforms and ML deployment pipelines.

You will drive data modeling, security, CI/CD, and governance, and partner with data science, product and platform leads to deliver scalable, compliant solutions.

10–15 years in data architecture with proven TB–PB scale lakehouse designs and production AI/ML solutions are required.

Qualifications

• 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.

Responsibilities

• Own architectural decisions for Lakehouse platforms and ML pipelines.
• Architect end‑to‑end data and AI solutions including medallion Lakehouse on ADLS Gen2 + Delta Lake.
• Design data models across PostgreSQL, Snowflake, Synapse; productionize ML/DL models.
• Enforce security and privacy protections (PII, GDPR) and governance.
• Build CI/CD and MLOps pipelines; instrument observability.
• Coach cross‑functional teams and align with CDO data policy.

Skills

Python

PySpark

SQL

Databricks

Delta Lake

Spark Structured Streaming

Azure stack

ADLS Gen2

ADF

Synapse

Azure ML

AKS

Event Hubs

Key Vault

Medallion Lakehouse

PostgreSQL

Snowflake

AI/ML

MLflow

PII/GDPR

CI/CD

MLOps

Docker

Kubernetes

Tools

Azure DevOps

GitHub Actions

Terraform

Bicep

Cloud security

Job description

Job Description

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

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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 typical 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 the 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