Solution Architect- Data and AI

MICHELIN France; Pune District; On-site; INR 4,200,000 - 7,000,000; Full time

Job summary

MICHELIN France in Pune seeks a seasoned Data & AI Solutions Architect to own architectural decisions and design end-to-end Lakehouse-based data and ML pipelines. You will arbitrate latency, cost, and scalability while aligning with global standards.

You will lead PySpark/Databricks workloads, build security and governance controls, and implement CI/CD and MLOps using Azure DevOps, Terraform/Bicep, Docker and Kubernetes, with hands-on delivery as needed.

Qualifications

• 10–15 years of experience in data architecture, data engineering or data science.
• Proven track record delivering enterprise Lakehouse and production AI/ML solutions.
• Expertise in data modeling, governance, and security
• Strong hands-on experience with Databricks, Delta Lake and Spark streaming
• Experience implementing CI/CD and MLOps pipelines with Azure DevOps or GitHub Actions, Terraform/Bicep, Docker, Kubernetes

Responsibilities

• Own architectural decisions (ADRs), reference blueprints and target-state designs.
• Architect end-to-end data and AI solutions including Lakehouse on ADLS Gen2 + Delta Lake and ML pipelines.
• Optimize large-scale PySpark/Databricks workloads (partitioning, AQE, skew handling, Photon).
• Design data models across PostgreSQL and Snowflake with Kimball/Data Vault 2.0 patterns.
• Productionize ML/DL models and expose via FastAPI endpoints.
• Enforce security and governance (PII, GDPR, Entra ID, Key Vault).
• Build CI/CD and MLOps pipelines (Azure DevOps, Terraform/Bicep, MLflow); instrument observability.
• Guide Data & AI Architecture design and aircraft STAR schema patterns.
• Suggest platform components best-fit to requirements.
• Implement Continuous Architecture and keep up with BI/AI trends.
• Collaborate across teams and influence stakeholders; hands-on when needed.

Skills

Advanced Python

PySpark

SQL

Databricks

Delta Lake

Spark Structured Streaming

Azure DevOps

GitHub Actions

Terraform

Bicep

Docker

Kubernetes

Tools

Databricks

Delta Lake

Spark Structured Streaming

Azure DevOps

GitHub Actions

Terraform

Bicep

Docker

Kubernetes

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.

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

Proven design of medallion Lakehouse architectures at TB-PB scale

Hands-off PII/GDPR data handling and cloud security

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

Communication Clarity

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

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

Data Mesh / federated governance experience