Summary
Overview
Work History
Education
Skills
Certification
Languages
Timeline
Generic

AMIT YADAV

Lucknow

Summary

AI/ML Engineer and MLOps Lead with 8+ years of experience delivering production-grade ML and Generative AI systems across GCP, Azure, and AWS. Expert in MLOps pipelines (Kubeflow, Vertex AI, SageMaker, Azure ML), CI/CD automation, and scalable deployments on Kubernetes and Terraform. Collaborated with Google’s Agent Development Kit (ADK) within the Gemini team, building multi-agent systems for cybersecurity, healthcare, and energy domains. Skilled in LLM fine-tuning, Hugging Face models, RAG pipelines, and vector databases for domain-specific GenAI solutions. Integrated LangChain, OpenAI tools, Tavus Virtual Avatars, and observability platforms (Phoenix, W&B, AgentOps) into enterprise AI workflows. Proven team lead and individual contributor, delivering measurable outcomes across banking, telecom, manufacturing, and renewable energy.

Overview

8
8
years of professional experience
1
1
Certification

Work History

AI Software Designer –GenAI, Google Collaboration

Globant
10.2024 - Current
  • Contributed in Google’s Agent Development Kit (ADK) as part of the Gemini/Generative AI team, enabling enterprise-scale agent frameworks and deployment of AI agents on Cloud Run and Vertex AI Agent Engine.
  • Designed and implemented multi-agent systems for clients in cybersecurity, energy, and healthcare domains, evaluating agent behavior, accuracy, and latency under production workloads.
  • Integrated LangChain tools, OpenAI functional tools, and custom toolchains into the ADK framework, expanding its interoperability and enabling richer agent workflows.
  • Delivered a multimodal agentic system by integrating Tavus Virtual Avatar with ADK’s Live API for a major banking customer, supporting video, audio, and text interactions.
  • Implemented observability and monitoring for multi-agent systems by integrating Arize Phoenix, Google Cloud native tracing, Weights & Biases (W&B), and AgentOps, ensuring transparency in function-calling, session tracking, and error analysis.
  • Developed and deployed A2A protocol–based multi-agent architectures with MCP tools and Vertex AI memory bank for contextualized session memory and reliable orchestration.
  • Acted as both team lead and individual contributor, guiding engineers, reviewing architecture, and delivering client-facing GenAI solutions in close collaboration with Google’s product and research teams.

Senior MIOps Engineer

Publicis Sapient Groupe
08.2023 - 08.2024
  • Developed and fine-tuned Large Language Models (LLMs), including Hugging Face models, using LoRA and transfer learning to enhance domain-specific NLP applications.
  • Implemented Retrieval-Augmented Generation (RAG) pipelines with vector databases (Milvus, Pinecone, PostgreSQL extensions) to enable semantic search and knowledge retrieval.
  • Designed and automated MLOps pipelines using Kubeflow Pipelines across multi-cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML Studio) for reproducible training, scalable deployment, and continuous monitoring.
  • Led a team of MLOps/ML engineers, mentoring members, reviewing design/architecture, and driving project execution while also contributing as an individual contributor on complex hands-on development tasks.
  • Built and orchestrated end-to-end CI/CD workflows with GitHub Actions, ensuring automated testing, versioning, and reliable delivery of ML services to production.
  • Deployed and managed Kubernetes clusters on GCP GKE, AWS EKS, and Azure AKS, applying best practices for scaling, workload isolation, and governance.
  • Developed and deployed microservices and APIs with FastAPI, secured and routed through APISIX, and integrated with cloud-native services for production-grade performance.
  • Provisioned and managed infrastructure as code with Terraform, covering compute, networking, and managed ML services across multi-cloud.
  • Containerized and versioned ML workloads using Google Container Registry (GCR), Azure Container Registry (ACR), and Amazon ECR, ensuring consistency across environments.
  • Applied RBAC, network policies, and pod security policies to secure Kubernetes clusters, aligned with enterprise security and compliance standards.
  • Set up monitoring and observability using Prometheus, Grafana, and ELK stack, integrated with Cloud Monitoring, CloudWatch, and Azure Monitor to track performance, latency, and anomalies.
  • Collaborated with cross-functional teams and stakeholders to deliver scalable, production-ready AI/ML solutions with measurable business outcomes.
  • Developed cost estimates and project specifications for proposals.

ML Engineer II- (MIOps-GCP)

General Mills
11.2022 - 08.2023
  • Provisioned and managed GKE clusters to deploy Kubeflow components and build end-to-end MLOps pipelines.
  • Containerized ML components with Docker for reproducibility and integrated them into Kubeflow Pipelines for scalable training and deployment.
  • Utilized TensorFlow Extended (TFX) for pipeline orchestration in Vertex AI, ensuring seamless integration with Google Cloud services.
  • Built automated CI/CD pipelines using GitHub Actions to streamline testing, container builds, and deployment workflows for ML services.
  • Designed and deployed ML models as Cloud Functions, enabling lightweight, serverless inference at scale.
  • Implemented demand forecasting pipelines leveraging BigQuery as the data warehouse for feature storage, training data, and analytics.
  • Orchestrated data pipelines with GCP Composer (Airflow DAGs) to automate ETL, preprocessing, and model training workflows.
  • Applied model monitoring and drift detection strategies to ensure ongoing reliability and accuracy of deployed models.
  • Integrated Cloud Storage for dataset and model artifact management with high availability and scalability.
  • Collaborated with cross-functional teams to deliver production-grade ML pipelines entirely on the GCP ecosystem.

Senior Data Scientist

Suzlon Group
03.2021 - 11.2022
  • Led a team of data scientists and ML engineers in designing and delivering predictive maintenance solutions for wind turbines, while also contributing as an individual contributor on deep learning and deployment tasks.
  • Designed and automated deep learning pipelines using 1D CNN models to detect anomalies in gearboxes and main bearing failures, deployed via Azure CI/CD pipelines with Docker for seamless production integration.
  • Built data preprocessing workflows in Python and automated them with Azure Data Factory, ensuring reliable ETL for model training and inference.
  • Developed and deployed a Computer Vision YOLO model for blade defect detection, integrated with drone imagery, and tracked experiments using MLflow for model logging and monitoring.
  • Created predictive maintenance models leveraging machine learning and time-series forecasting to detect main bearing failures from oil sample and SCADA data.
  • Applied advanced mathematical techniques (e.g., Fast Fourier Transform, Hilbert Transform) to analyze rotor vibrations in DFIG-based turbines for early fault detection.
  • Built Suzlatics, a predictive analytics application for monitoring turbine main bearing temperatures, with visualization and reporting in Python.
  • Utilized Azure Databricks and Snowflake for scalable data processing, integration, and analytics across the predictive maintenance ecosystem.

Machine learning Engineer

Thyssenkrupp Inc.
01.2020 - 02.2021
  • Architected and deployed end-to-end AI/ML solutions on Azure Machine Learning, implementing automated ML pipelines for automotive parts demand forecasting (Volkswagen Portugal) using Facebook Prophet.
  • Designed and implemented Azure DevOps CI/CD pipelines with YAML and the Azure ML SDK, automating model training, testing, and deployment workflows.
  • Set up model monitoring and experiment tracking using Azure ML Studio and MLflow, enabling real-time performance monitoring, drift detection, and governance.
  • Built and deployed Computer Vision models (YOLOv5) on Azure Kubernetes Service (AKS) for real-time defect detection in industrial conveyor systems, using Azure Container Registry for image management.
  • Implemented Azure IoT Edge solutions for deploying SSD object detection models with OpenCV at the edge for steel plate quality control, leveraging Azure IoT Hub for device management.
  • Developed and deployed LSTM models for time-series forecasting, served via Azure ML endpoints and integrated with Azure Synapse Analytics for downstream processing.
  • Applied MLOps best practices including model versioning, A/B testing, and automated retraining using Azure ML pipelines and Azure Functions for event-driven model updates.

Data analyst

Convergys India Pvt. Ltd
05.2017 - 12.2019
  • Collected, interpreted, and analyzed large-scale telecommunication datasets, delivering insights to support business decision-making and customer strategy.
  • Performed predictive analytics (regression and classification) to forecast customer trends and improve retention strategies.
  • Designed and implemented machine learning workflows (data processing, supervised and unsupervised learning) to enhance data analysis capabilities.
  • Developed and deployed a conversational chatbot for customer support using NLP techniques, improving self-service query handling.
  • Applied statistical sampling methods and developed custom statistical functions in Python to ensure accuracy and reliability in analysis.
  • Built reporting dashboards and visualizations with Power BI and Tableau, supporting business leaders with real-time KPIs.
  • Retrieved and processed large datasets using MySQL queries on AWS Redshift, integrating cloud data warehousing into analytical workflows.

Education

Executive MS - Artificial Intelligence

Aegis School of Business and Data Science
Powai, Mumbai
01.2020

Skills

LLMs

Agentic framework(LangGraph, Adk)

Generative AI

MLOps

Vertex AI

Amazon Bedrock

OpenAI

Sagemaker

Github Action CI/CD

Kubeflow

Data Science

TensorFlow Extended

Kubernetes

Istio

Computer Vision

OpenCV

NLP

Deep Learning

Machine Learning

Signal Processing

Voice Analysis

APISIX

FastAPI

AWS

Azure

GCP

Docker

Prometheus

Grafana

ELK Stack

RBAC

Network Policies

ConfigMaps

Secrets

MTLS

Cloud Functions

Cloud Storage

Cloud Pub/Sub

Terraform

Demand Forecasting

Python

GCP Composer

Airflow

BigQuery

SQL

Flask

ML Flow

Azure DevOps

ETL

Azure Data Factory

Azure Databricks

Snowflake

Forecasting

AWS Forecasting

Time Series Analysis

Certification

Google Cloud Professional Machine Learning Engineer (Issued Feb 2025)

  • Validates expertise in: ML model design, training, deployment, automation, and monitoring on GCP.
  • Key Skills: Vertex AI, BigQuery ML, AutoML, ML APIs, TensorFlow Extended (TFX), MLflow integration, Responsible AI, model evaluation & retraining.
  • Area of Expertise: End-to-end ML lifecycle on Google Cloud with production-grade automation and compliance.

Google Cloud Professional Cloud Architect (Issued June 2024)

  • Validates expertise in: Designing, building, and securing scalable cloud infrastructure on GCP.
  • Key Skills: GKE/Kubernetes, IAM & security, networking, storage systems, VPC design, hybrid/multi-cloud architecture.
  • Area of Expertise: Architecting resilient, cost-optimized, and secure AI/ML and data platforms on GCP.

Google Cloud Professional Data Engineer (Certified – Year to update)

  • Validates expertise in: Designing, building, operationalizing, and optimizing data pipelines and data processing systems.
  • Key Skills: BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, SQL optimization, ETL/ELT pipelines.
  • Area of Expertise: Data engineering for AI/ML workflows — ingestion, transformation, warehousing, and real-time analytics.

Languages

English, Hindi and German(A1)

Timeline

AI Software Designer –GenAI, Google Collaboration

Globant
10.2024 - Current

Senior MIOps Engineer

Publicis Sapient Groupe
08.2023 - 08.2024

ML Engineer II- (MIOps-GCP)

General Mills
11.2022 - 08.2023

Senior Data Scientist

Suzlon Group
03.2021 - 11.2022

Machine learning Engineer

Thyssenkrupp Inc.
01.2020 - 02.2021

Data analyst

Convergys India Pvt. Ltd
05.2017 - 12.2019

Executive MS - Artificial Intelligence

Aegis School of Business and Data Science

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AMIT YADAV