Solution Architect- Data And AI

Michelin Americas Research; Pune District; On-site; INR 4,000,000 - 9,000,000; Full time

Job summary

Michelin Americas Research is seeking a Solution Architect- Data and AI in Pune to own architectural decisions, reference blueprints, and target-state designs. You will lead end-to-end data and AI solutions, including medallion Lakehouse on ADLS Gen2, streaming ingestion, and ML deployment pipelines.

Expect to optimize large-scale PySpark/Databricks workloads, enforce security/compliance (PII, GDPR), and build CI/CD/MLOps pipelines.

Qualifications

• 10–15 years of experience in data architecture and data engineering/data science.
• Proven delivery of at least one enterprise Lakehouse and one production AI/ML solution.
• Deep DB expertise: PostgreSQL, Snowflake, Synapse and ADLS.
• Strong design of medallion Lakehouse architectures at TB–PB scale.
• Hands-on data privacy, security and GDPR considerations.

Responsibilities

• Own ADRs, reference blueprints and target-state designs; arbitrate trade-offs on latency, cost, and scalability.
• Architect end-to-end data and AI solutions across medallion Lakehouse platforms and pipelines.
• Coach delivery teams and align with Data Science, Product, QA, and Platform leads.
• Implement CI/CD and MLOps pipelines; instrument observability.
• Evaluate platform components for given requirements and decide best fit.

Skills

Python

PySpark

SQL

Databricks

Delta Lake

Spark Streaming

PostgreSQL

Snowflake

Synapse

MLflow

PII/GDPR handling

Azure ADLS Gen2

ADF

Azure ML

AKS

Event Hubs

Key Vault

CI/CD

MLOps

Terraform

Kubernetes

Docker

Tools

Azure DevOps

GitHub Actions

Terraform

Bicep

Docker

Kubernetes

Job description

Job Description

Solution Architect- Data and AI

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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 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