Solution Architect

Michelin; Pune District; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Michelin is seeking a seasoned Solution Architect for AI and Data to own end-to-end architectural decisions, design medallion Lakehouse solutions on ADLS Gen2 + Delta Lake, and lead ML deployment pipelines. You will optimize large PySpark/Databricks workloads and craft scalable data models across PostgreSQL, Synapse and Snowflake.

Ideal candidates have extensive Azure experience, strong security practices for PII/GDPR, and hands-on MLOps expertise to align with business strategy.

Qualifications

• 10–15 years in data architecture, data engineering or data science.
• Demonstrated delivery of at least one enterprise Lakehouse and one production AI/ML solution.
• Expert in Databricks, Delta Lake and Spark Structured Streaming.
• Strong Azure stack coverage (ADLS Gen2, ADF, Azure ML, AKS, Event Hubs, Key Vault).
• DBMS proficiency across PostgreSQL, Synapse, Snowflake and ADLS.
• Hands-on ML/DL fluency (scikit-learn, TensorFlow, PyTorch, MLflow).
• Experience with PII/GDPR data handling and cloud security.
• CI/CD & MLOps: Azure DevOps or GitHub Actions, Terraform/Bicep, Docker, Kubernetes.

Responsibilities

• 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 including Lakehouse 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, Synapse, Snowflake; utilize Kimball, Data Vault 2.0 patterns.

Skills

Python

PySpark

SQL

Databricks

Delta Lake

Spark Streaming

Azure

Azure Data Factory

Azure ML

AKS

Event Hubs

Key Vault

PostgreSQL

Synapse

Snowflake

MLflow

scikit-learn

TensorFlow

PyTorch

PII Handling

CI/CD

MLOps

Docker

Kubernetes

Terraform

Tools

Databricks

Delta Lake

Synapse

Snowflake

Power BI

Job description

Position Title- Solution Architect - AI and Data

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.

What You Bring

Must-Have

• Advanced Python, PySpark, SQL
• Expert in Databricks, Delta Lake, Spark Structured Streaming
• Strong Azure stack: ADLS Gen2, ADF, Azure ML, AKS, Event Hubs, Key Vault
• Proven design of medallion Lakehouse architectures at TBPB 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

Nice-to-Have

• Azure OpenAI, AI Search, RAG, vector databases
• IoT ingestion patterns
• Data Mesh / federated governance experience

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.