Surajit Dhar

AI Researcher

Hyderabad, Telangana, India25 yrs 5 mos experience

Highly StableAI Enabled

Key Highlights

20+ years of experience in Data & AI architecture.
Expert in designing enterprise-scale ML systems on Azure.
Strong leadership in mentoring and project management.

Stackforce AI infers this person is a SaaS-focused Data & AI Architect with extensive experience in enterprise ML systems.

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Skills

Core Skills

Ml EngineeringCloud InfrastructureData QualityGovernanceData EngineeringConsulting

Other Skills

Agile MethodologiesAnomaly DetectionArchitecture & MLOpsAzure MLBicepBig DataBusiness IntelligenceData GovernanceData ManagementData MiningData ModelingData WarehousingDimensional ModelingETL ArchitecturesEnterprise Architecture

About

I am a Data & AI Architect and Senior Data & Applied Scientist with 20+ years of experience spanning data engineering, machine learning, and enterprise cloud architecture. My work sits at the intersection of ML Engineering, Cloud Infrastructure, and Applied AI. I design and deploy enterprise-scale ML systems on Azure that are resilient, governed, and business-impact driven. I also bring strong leadership experience — having led vendor teams on Data & AI projects, and mentored junior and mid-level team members to grow into effective data professionals. Recent work highlights: ML Engineering & MLOps: Designed ML pipelines in Azure ML, integrating model training, validation, and inference with full observability. Built custom logging frameworks (AMLLogger, error catalogs, correlation IDs) integrated with App Insights & Log Analytics. Infrastructure as Code (IaC): Automated deployment of ML services, ADF pipelines, and monitoring components using Bicep templates and EV2 Safe Deployment Practices (SDP), ensuring compliance and reproducibility. Data Quality & Drift Detection: Architected statistical fingerprinting frameworks (PSI, KS, entropy, Cramér’s V) for anomaly detection across large-scale ingestion pipelines. Partner Skilling Analytics: Built recommendation systems to identify high-potential candidates for Microsoft certifications, accelerating specialization attainment in Data & AI Solution Areas. I thrive on designing architectures that bring together ML, cloud infrastructure, and governance—bridging the gap between data science innovation and enterprise-grade deployment.

Experience

25 yrs 5 mos

Total Experience

6 yrs 4 mos

Average Tenure

Current Experience

Microsoft

2 roles

Sr Data & Applied Scientist

Promoted

Nov 2015 – Sep 2025 · 9 yrs 10 mos

Enterprise ML at scale: Architected end-to-end pipelines on Azure ML and automated deployments with Bicep + EV2, ensuring both compliance and agility.
Data quality and anomaly detection: Built a drift and anomaly detection framework powered by statistical “fingerprints” of business data. By combining adaptive binning with tests such as Chi-Square, KS, PSI, and Cramér’s V, we enabled early anomaly detection and improved data reliability across pipelines.
Partner enablement through AI: Created a certification recommendation model that analyzed candidate learning paths and suggested certifications aligned with the Microsoft Partner Competency Framework. This helped partners advance their Data & AI competency and improved specialization success rates with >80% precision.
Operational efficiency with LLMs: Delivered an LLM-based incident categorization system that reduced noise by ~40%, automatically clustering issues into actionable categories and freeing up analyst bandwidth.
Responsible AI and governance: Collaborated with Microsoft’s Central Governance Team to build a model registration catalog and embed Responsible AI guardrails — hallucination detection, jailbreak prevention, fairness, and transparency. This framework strengthened oversight and trust in GenAI systems.
Data governance innovation: Combined advanced NLP and clustering techniques to create digital fingerprints of Azure Data Lake assets, making them easier to govern and discover across the enterprise. In parallel, developed a Data Lifecycle Management Framework that classified assets by compliance requirements, access frequency, and estimated carbon footprint. Automated tiering policies seamlessly shifted data between hot, cool, and cold storage, driving down storage costs, improving compliance, and supporting Microsoft’s sustainability commitments.

ML EngineeringMLOpsAzure MLData QualityAnomaly DetectionResponsible AI+2

SR SDE

Mar 2007 – Oct 2015 · 8 yrs 7 mos

Designed dimensional models & ETL architectures for business-critical processes.
Created identity matching framework for trade screening, reducing false positives by 5–15%
Built data lifecycle management frameworks to improve scalability and governance.
Delivered performance optimizations via query tuning, partitioning, and architecture improvements.

Dimensional ModelingETL ArchitecturesPerformance OptimizationData Engineering

Iflex

Consultant

Mar 2005 – Mar 2007 · 2 yrs

Part of the core product team that designed and implemented the Collections Module within the Retail Banking Suite, streamlining debt management and recovery processes for financial institutions.

Product DesignImplementationConsulting