Anik Mukhopadhyay - Senior AI/ML Engineer

Anik Mukhopadhyay

Senior AI/ML Engineer

11+ Years Experience • Azure AI • GenAI • MLOps • Agentic AI
● Senior AI/ML Talent
Experience 11+ Years
Current Role Senior AI/ML Engineer
Core Focus Azure AI / GenAI / MLOps
Domains Healthcare / Finance / Retail
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Professional Summary

  • Results-driven AI/ML Engineering Professional with 11+ years of experience designing, developing, and deploying enterprise-scale AI, Machine Learning, and Generative AI solutions across Healthcare, Finance, Retail, and Manufacturing domains.
  • Proven expertise in building production-grade, cloud-native AI platforms leveraging Large Language Models, Retrieval-Augmented Generation, Agentic AI architectures, Deep Learning, and MLOps frameworks.
  • Extensive hands-on experience with Microsoft Azure technologies including Azure Machine Learning Studio, Azure Kubernetes Service, Azure Blob Storage, Azure Data Factory, and Azure DevOps Pipelines.
  • Skilled in deploying, orchestrating, and managing ML, AI, and Generative AI models within Kubernetes and Azure Kubernetes Service environments.
  • Strong expertise in designing end-to-end machine learning pipelines covering data ingestion, feature engineering, model training, inference, monitoring, retraining, automation, and continuous improvement.
  • Proficient in MLOps practices using MLflow for experiment tracking, model registry, versioning, logging, monitoring, reproducibility, and governance.
  • Experienced in building CI/CD workflows using Git and Azure DevOps to automate model deployment, infrastructure management, testing, and reliable production releases.
  • Strong Python programming expertise covering environment setup, package management, automation scripting, coding standards, reusable frameworks, and code optimization.
  • Experienced with code quality and formatting tools including Black, Flake8, pylint, and linting frameworks for clean and production-ready codebases.
  • Demonstrated experience leading cross-functional teams and collaborating with stakeholders to deliver high-impact AI solutions that improve operational efficiency and customer experiences.
  • Strong background in cloud computing, distributed systems, model operationalization, and scalable AI infrastructure across Azure and AWS environments.

Work History

EY GDS

Senior AI/ML Engineer
April 2026 – Present
  • Designed and implemented an enterprise-scale Azure-native AI/GenAI platform for post-call transcription, AI-generated summarization, conversational intelligence, and transcript-grounded analytics.
  • Leveraged Azure OpenAI, Azure Speech Services, Azure AI Search, Cosmos DB, Azure Machine Learning Studio, and Azure Functions to build enterprise AI capabilities.
  • Deployed and managed ML, AI, and GenAI models using Azure Machine Learning Studio and Azure Kubernetes Service for scalable hosting, inference, and orchestration.
  • Architected containerized AI workloads within AKS clusters with secure, highly available, and auto-scalable deployment patterns.
  • Designed scalable ML pipelines for training, inference, monitoring, retraining, and automation using Azure ML pipelines, Azure Data Factory, and Azure DevOps.
  • Developed event-driven pipelines for secure ingestion, transcription, indexing, summarization, and retrieval of telephony call recordings.
  • Implemented enterprise MLOps practices including model versioning, deployment automation, monitoring, rollback strategies, and lifecycle governance.
  • Used MLflow for experiment tracking, model registry management, logging, monitoring, and reproducible machine-learning workflows.
  • Created CI/CD workflows using Git and Azure DevOps Pipelines for automated build, testing, deployment, and release management.
  • Developed Python-based automation frameworks and backend services for AI orchestration, data processing, model integration, and operational workflows.
  • Managed Python environments, dependencies, virtual environments, and package configurations for scalable ML application development.
  • Applied Black, Flake8, pylint, and linting frameworks to maintain enterprise coding standards and production-ready code.
  • Architected RBAC-based access control and Azure Entra ID integration for controlled transcript visibility, federation, audit logging, and governance.
  • Implemented private endpoints, managed identities, encryption standards, and network isolation for secure AI platform operations.
  • Collaborated with IAM, security, telephony, enterprise architecture, and compliance teams to define scalable AI solution architecture.
  • Led architecture discovery sessions, vendor integration analysis, and technical governance discussions for enterprise AI deployments.
  • Built automated data ingestion and transformation workflows using Azure Data Factory for structured and unstructured enterprise data.
  • Monitored model performance, inference latency, operational health, and resource utilization across production AI environments.
  • Supported deployment automation, release orchestration, and infrastructure provisioning across development, QA, and production environments.
  • Optimized AI/ML workloads for scalability, resiliency, fault tolerance, and cost efficiency across distributed Azure environments.

Optum UHG

AI/ML Engineer
Dec 2025 – March 2026
  • Designed and implemented a HIPAA-compliant Healthcare RAG platform leveraging GPT-4, OpenAI Embeddings, PostgreSQL PgVector, and cloud-native services.
  • Built secure context-aware querying capabilities across clinical documentation, insurance records, patient policies, and hospital SOPs.
  • Engineered document ingestion and preprocessing workflows using AWS Textract, Apache Airflow, Azure Data Factory, and Python automation.
  • Implemented OCR processing, metadata extraction, PHI masking, and semantic chunk generation for healthcare documents.
  • Integrated Microsoft Presidio and custom NLP pipelines to anonymize sensitive healthcare information.
  • Developed section-aware retrieval pipelines and citation-driven response generation to improve grounding and traceability.
  • Built RBAC mechanisms, audit logging, encryption strategies, and similarity-threshold validation for AI governance and compliance.
  • Deployed containerized AI/ML microservices using Docker and Azure Kubernetes Service.
  • Used Azure Machine Learning Studio to train, register, version, and deploy ML and GenAI models.
  • Implemented end-to-end MLOps workflows using MLflow for experiment tracking, model registry, artifact logging, and performance monitoring.
  • Developed scalable ML pipelines for ingestion, training, inference, evaluation, and monitoring using Python and Azure ML pipelines.
  • Developed vector-search and embedding pipelines using PostgreSQL PgVector and Azure Blob Storage.
  • Automated CI/CD using GitHub Actions and Azure DevOps Pipelines for infrastructure provisioning, container deployment, testing, and application delivery.
  • Established Python development standards including virtual environments, dependency management, modular architecture, and reusable utility frameworks.
  • Integrated Black, Flake8, pylint, and linting frameworks into automated DevOps pipelines.
  • Configured DataDog, Azure Monitor, and centralized logging for application health, model performance, infrastructure metrics, and reliability.
  • Performed RAG evaluation and retrieval benchmarking using RAGAS and custom evaluation metrics.
  • Collaborated with data scientists, security teams, DevOps engineers, and healthcare stakeholders to deliver production-grade GenAI solutions.

GlobalLogic

Consultant AIML Engineer
Jan 2024 – Dec 2026
  • Developed an end-to-end Customer Intent Prediction platform using Python, MLflow, and Azure ML for chat, email, and support-ticket classification.
  • Designed scalable MLOps pipelines using MLflow experiment tracking, model registry, automated retraining, CI/CD, and Docker-based Kubernetes deployments.
  • Built feature engineering and NLP preprocessing workflows using Airflow and FastAPI.
  • Implemented model monitoring and drift detection workflows using Prometheus and supporting observability tools.
  • Developed secure real-time inference APIs with automated validation, centralized logging, version control, and monitoring dashboards.
  • Supported continuous model governance and production ML operations.

LTM

Senior Data Scientist
Oct 2021 – Jan 2024
  • Developed an enterprise-grade Predictive Maintenance platform using Azure Machine Learning, MLflow, Apache Spark, and Kafka.
  • Designed scalable ML pipelines for high-volume streaming and historical equipment data.
  • Built end-to-end MLOps workflows covering automated retraining, model versioning, drift detection, and deployment automation.
  • Implemented CI/CD pipelines using Azure DevOps and MLflow for model integration, testing, and production deployment.
  • Developed FastAPI RESTful inference microservices and Docker-based applications for scalable model serving.
  • Deployed ML workloads on Azure Kubernetes Service with auto-scaling and Terraform-based infrastructure provisioning.
  • Configured Infrastructure-as-Code deployments using Terraform for environment setup and resource management.
  • Implemented monitoring, logging, and alerting using Prometheus, Grafana, and Azure Monitor.
  • Developed anomaly detection pipelines for abnormal equipment behavior and proactive maintenance alerts.
  • Collaborated with data engineering, DevOps, and business teams to deliver scalable predictive-maintenance solutions.

Mobius Knowledge Services

Data Scientist
Oct 2021 – Jan 2024
  • Designed and implemented a real-time Fraud Detection platform using Azure ML, MLflow, Apache Kafka, and FastAPI.
  • Developed scalable MLOps pipelines with automated retraining, drift detection, CI/CD integration, and MLflow-based governance.
  • Containerized and deployed inference services on Azure Kubernetes Service using auto-scaling and canary deployments.
  • Developed low-latency APIs for real-time fraud prediction and transaction risk scoring.
  • Configured Prometheus, Grafana, and Azure Monitor for operational monitoring and compliance.
  • Integrated streaming and batch data pipelines for high-volume transactional analytics.
  • Collaborated with data engineering, security, and business teams to design secure fraud analytics solutions.
  • Automated deployment workflows and infrastructure provisioning using CI/CD pipelines.
  • Performed model performance analysis, monitoring, and tuning to improve prediction accuracy and reduce false positives.

Fidus Professionals Pvt Ltd

Data Scientist
Feb 2016 – May 2021
  • Developed an AI-powered Demand Forecasting platform using Azure Machine Learning, MLflow, Apache Spark, and LSTM models.
  • Designed scalable ML pipelines for demand prediction and real-time forecasting across retail operations.
  • Built enterprise MLOps workflows covering automated retraining, CI/CD deployment, model versioning, and lifecycle management.
  • Implemented model drift detection and performance monitoring for forecasting reliability.
  • Containerized ML services using Docker and deployed forecasting APIs on Azure Kubernetes Service.
  • Developed RESTful APIs using FastAPI for demand forecasting models and downstream retail applications.
  • Configured Kubernetes auto-scaling and resource optimization for high-volume forecasting workloads.
  • Collaborated with business, data engineering, and analytics teams to deliver AI-driven retail solutions.
  • Optimized distributed data processing using Apache Spark for large-scale transaction and inventory datasets.
  • Supported model deployment, testing, monitoring, and production activities following MLOps and cloud governance practices.

Skillsets

Generative AI LLM Engineering GPT-4 Llama LangChain LangGraph Agentic AI RAG Prompt Engineering AI Evaluation Vector Databases Embeddings AI Governance BERT Fine-Tuning Machine Learning NLP Deep Learning PyTorch TensorFlow Hugging Face CNN LSTM Time-Series Forecasting Random Forest Decision Tree XGBoost Logistic Regression Azure ML Azure OpenAI Azure AI Search Azure Data Factory Azure Blob Storage AKS Azure DevOps AWS Docker Kubernetes MLflow Kubeflow CI/CD Python PySpark Databricks Airflow SQL FastAPI Flask REST APIs Microservices HIPAA GDPR RBAC OAuth2 JWT Encryption Audit Logging

Applications & Tools

Generative AI & LLM
GPT-4 Llama LangChain LangGraph RAG Prompt Engineering Embeddings Vector Databases BERT Fine-Tuning
Azure & Cloud
Azure Machine Learning Azure OpenAI Azure AI Search Azure Speech Services Azure Blob Storage Azure Data Factory Azure Functions Azure Kubernetes Service Azure DevOps Cosmos DB Azure Monitor AWS
Machine Learning
PyTorch TensorFlow Hugging Face Scikit-learn NLP Deep Learning CNN LSTM XGBoost Random Forest Decision Tree Logistic Regression
Data Engineering
PySpark Databricks Apache Spark Apache Kafka Azure Data Factory Airflow SQL ETL
Backend & APIs
Python FastAPI Flask REST APIs Microservices
MLOps & DevOps
MLflow Kubeflow Docker Kubernetes Azure DevOps Git CI/CD Prometheus Grafana Terraform
Code Quality
Black Flake8 Pylint Linting Python Environments Dependency Management
Security & Compliance
HIPAA GDPR RBAC OAuth2 JWT Azure Entra ID Private Endpoints Managed Identities

Certifications & Awards

  • AI Dojo Certification — Generative AI and Agentic AI based on Healthcare domain — Optum.
  • Microsoft Certified Azure ML Engineer.
  • Certification in Data Science and Python — Edureka.
  • Optum — Bravo! Diamond Award.
  • GlobalLogic — Spotlight of the Month Award — Two Times.
  • PGD AIML Extra Mile Award — Top Achiever in AI and ML — NIT Warangal.

Key Projects

Pfizer AURA — AI-Powered Call Summarization & Transcript Intelligence

Designed and developed an enterprise-grade Azure-native AI platform for automated call transcription, AI-generated summarization, and transcript-grounded conversational intelligence within a regulated healthcare environment using Azure OpenAI, Azure Speech Services, Azure AI Search, Cosmos DB, and Azure Functions.

Optum.ai — HIPAA-Compliant Healthcare Policy RAG Assistant

Designed and deployed an enterprise-grade RAG platform for secure, citation-grounded retrieval of PHI-containing healthcare documents, supporting compliant policy lookup, auditability, and trustworthy clinical decision support.

T-Mobile — Customer Intent Prediction & Predictive IVR Optimization

Built a predictive ML platform to classify customer call intent and forecast call propensity, enabling proactive IVR optimization, self-service automation, reduced agent workload, and improved customer experience.

Enterprise Customer Intent Prediction & Intelligent Ticket Routing

Designed an AI platform to predict customer intent from omnichannel support interactions and automatically route tickets to relevant teams using NLP-driven prioritization and workflow automation.

Predictive Maintenance & Equipment Failure Forecasting

Developed an AI-powered predictive maintenance platform using IoT sensor telemetry, equipment logs, and time-series forecasting to proactively identify potential failures, minimize downtime, and optimize maintenance scheduling.

Financial Fraud Detection & Real-Time Risk Scoring

Built a real-time fraud detection and transaction risk-scoring platform using machine learning, anomaly detection, and streaming analytics for low-latency financial risk decisions and regulatory compliance.

Notable Achievements

  • Built enterprise-scale AI and Generative AI platforms across Healthcare, Finance, Retail, and Manufacturing domains.
  • Designed production-grade Azure-native AI platforms integrating Azure OpenAI, Azure AI Search, Azure Speech Services, Azure ML, AKS, Cosmos DB, and Azure Functions.
  • Architected HIPAA-compliant RAG solutions for sensitive healthcare information with citation-grounded retrieval and PHI protection.
  • Implemented enterprise MLOps frameworks using MLflow for model tracking, registry, versioning, monitoring, and governance.
  • Developed Agentic AI, RAG, LLM, NLP, and Deep Learning solutions for enterprise use cases.
  • Built real-time fraud detection and predictive maintenance platforms using Azure ML, Kafka, Spark, FastAPI, and Kubernetes.
  • Developed intelligent customer intent prediction and ticket-routing platforms for enterprise customer-support operations.
  • Implemented secure enterprise AI architecture using RBAC, Azure Entra ID, private endpoints, managed identities, encryption, and audit logging.
  • Built CI/CD and automated deployment frameworks using Git, Azure DevOps, GitHub Actions, Docker, Kubernetes, and Terraform.
  • Recognized with the Optum Bravo! Diamond Award for outstanding contribution.
  • Received the GlobalLogic Spotlight of the Month Award twice.
  • Received the Extra Mile Award as a Top Achiever in AI and ML from NIT Warangal.

Education

Post Graduation Diploma — Artificial Intelligence & Machine Learning
National Institute of Technology Warangal (NIT) • 2020 – 2022

Bachelor of Technology — Computer Science
West Bengal University of Technology (WBUT) • 2012 – 2016
Candidate profile presented by Ace Technologies • AI / ML • Generative AI • Azure • MLOps