Amit Gupta

AI Researcher

Bengaluru, Karnataka, India9 yrs 2 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in deploying scalable AI systems using LLMs.
Proven track record in MLOps and cloud-native infrastructure.
Skilled in building agentic AI platforms for personalized recommendations.

Stackforce AI infers this person is a SaaS expert specializing in AI and MLOps.

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Skills

Core Skills

Agentic Ai DevelopmentMlopsCloud ApplicationsDevopsMachine Learning

Other Skills

Google Kubernetes Engine (GKE)Model Context Protocol (MCP)Large Language Model Operations (LLMOps)Cloud DevelopmentKubernetesDockerFastAPISemantic SearchRecommendation SystemsLarge Language Models (LLMs)Retrieval-Augmented Generation (RAG)Google Cloud Platform (GCP)AWS SageMakerAzure PipelinesTerraform

About

🔧 Sr. AI Engineer | GenAI | Agentic AI | LLM Systems | MLOps | Kubernetes | Cloud AI I design and deploy production-grade GenAI and machine learning systems powered by Large Language Models (LLMs), retrieval-augmented generation (RAG), and intelligent orchestration. I specialize in building agentic AI platforms that understand user intent, perform multi-constraint retrieval, and generate grounded, explainable recommendations at scale. My experience includes developing and operationalizing end-to-end ML pipelines, conversational AI systems, and real-time inference services using Kubernetes, Docker, FastAPI, and cloud-native infrastructure. I focus on building scalable, reliable, and observable ML systems that operate efficiently in production environments. Skilled in LLMs, GenAI, RAG, semantic search, recommendation systems, MLOps, Kubernetes, and cloud platforms, I’m passionate about solving complex problems and enabling scalable AI in production. Let’s connect if you're working on GenAI, Agentic AI, LLM platforms, or production ML systems.

Experience

9 yrs 2 mos

Total Experience

2 yrs 9 mos

Average Tenure

10 mos

Current Experience

Best buy

Sr. AI Engineer

Jul 2025 – Present · 10 mos · India · Hybrid

Designed and deployed Conversational AI and Agentic AI systems to power intelligent product discovery, leveraging Model Context Protocol (MCP), agent orchestration, and contextual memory to understand user intent, extract structured signals, and deliver personalized, explainable recommendations.
Built agent workflows using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), semantic search, and multi-constraint ranking, enabling grounded recommendations and improving customer engagement and discovery efficiency.
Architected and deployed scalable ML inference and agent services on Google Cloud Platform (GCP) using Kubernetes (GKE), Docker, and cloud-native microservices, ensuring high availability, scalability, and production reliability.
Optimized Search 2.0 retrieval pipelines, reducing latency and improving response times through orchestration improvements, caching strategies, and efficient service communication.
Developed automated regression and evaluation suites to validate LLM and retrieval performance, improving NLU intent accuracy, recommendation relevancy, and cluster quality, and reducing production regressions.
Deployed real-time inference services using FastAPI, Kubernetes, and Seldon Core, enabling scalable model deployment and modular agent integration via MCP.
Implemented observability, tracing, and monitoring using Langfuse and cloud-native tools, improving system reliability, debugging efficiency, and performance visibility.
Tech Stack: Model Context Protocol (MCP), FastMCP, MCP Agents, Google ADK, Langfuse, Python, LLMs, GenAI, RAG, GCP, Vertex AI, Kubernetes (GKE), Docker, FastAPI, Seldon Core, Semantic Search, Recommendation Systems, ML Deployment, MLOps

Agentic AI DevelopmentGoogle Kubernetes Engine (GKE)Model Context Protocol (MCP)Large Language Model Operations (LLMOps)Cloud ApplicationsCloud Development+6

Alcon

2 roles

Principal I AI Operations

Promoted

May 2023 – Jun 2025 · 2 yrs 1 mo

Specializing in creating end-to-end framework solutions for data scientists, leveraging robust and scalable tools and platforms to streamline machine learning workflows. With expertise in AWS SageMaker, Azure Pipelines, Terraform, Docker, and Kubernetes, I enable teams to deploy and manage ML models efficiently in production.
✅ Designing and delivering reusable framework solutions to accelerate machine learning model development and deployment.
✅ Expertise in cloud-native platforms for scalable and reliable ML operations.
✅ Building CI/CD pipelines in Azure for seamless integration and deployment.
✅ Implementing infrastructure-as-code solutions using Terraform to ensure reproducibility and scalability.
✅ Enabling containerized deployments with Docker and Kubernetes for robust, portable ML services.

AWS SageMakerAzure PipelinesTerraformDockerKubernetesDevOps+1

Machine Learning Engineer

Oct 2020 – May 2023 · 2 yrs 7 mos

Designed an on-prem MLOps platform to provide end-to-end services for frictionless training, validation, deployment, and monitoring of ML models based on Open-Source software, including a CI/CD component for ML.
Added model pre-deployment tests in CI/CD pipelines to ensure model, training, and data consistency.
Developed Argo Workflow for monitoring of models in production, using NannyML, Kubernetes, AWS SQS, and S3 Event notifications, allowing for observability of performance and feature drift.

MLOpsAWSCloud DevelopmentKubernetes

Honeywell

Data Scientist

Mar 2018 – Oct 2020 · 2 yrs 7 mos · Bangalore

Equipment Rental Price Optimization:**
Generated optimized bids for pipeline welding equipment based on historical and refurbishment data.
Developed and deployed a predictive Web-App on AWS EC2 using Python, Flask, BOTO3, HTML, Bootstrap, and AJAX.
Built a machine learning model with Regression, Decision Trees, and Random Forest, achieving an Adjusted R-Squared of 0.86.
Integrated backend services for real-time equipment availability updates from Kanban.
Financial Performance Analysis:**
Extracted and processed SAP SD module data for financial analysis.
Utilized scikit-learn pipeline for data cleaning.
Implemented Pareto 80-20 principle for key performance indicators in Power BI with DAX code.
Identification of Personal Protective Equipment (PPE):**
Integrated YOLO object detection algorithm for Helmet and Ear Muffs detection.
Automated image labeling using OpenCV, reducing training time by 45%.

AWSMachine Learning AlgorithmsPythonFlaskHTMLBootstrap+2

Big data bizviz

Associate Decision Scientist

Feb 2017 – Mar 2018 · 1 yr 1 mo · Bengaluru Area, India

Hands on with XGBoosting, LightXGBoost, Decision Trees, Random Forest ,Multiple Regression, Logistic Regression, SVM, KNN, Naïve Bayes, Time Series Analysis, Market Mix Model and Recommendation systems.

XGBoostingDecision TreesRandom ForestLogistic RegressionMarket Mix Model