Headline

MLOps Engineer | GenAI | RAG | MLOps | Machine Learning | Python | PyTorch | AWS | Azure | Kubernetes | Terraform

About

I'm an AI/ML Engineer with 4.5 years of experience building cloud-native platforms, automating ML workflows, and developing production-ready AI systems. My expertise spans MLOps, Generative AI, RAG, Agentic AI, cloud engineering, and AI observability. I focus on bridging the gap between AI experimentation and production by building systems that are scalable, reliable, secure, and efficient. Generative AI & Agentic AI: Experience with LLM applications, RAG pipelines, semantic search, vector databases, embeddings, retrieval optimization, local LLM inference, tool calling, and agent orchestration. Interested in building reliable AI agents and integrating LLMs with APIs and enterprise systems. MLOps & AI Observability: Involved in building an enterprise AI/ML observability platform for repository quality, automated testing, pipeline reliability, drift detection, evaluation, monitoring, and reporting. Focused on extending observability and evaluation practices to GenAI and agentic systems. Cloud & Platform Engineering: Hands-on experience across AWS and Azure, with a strong foundation in Python, Docker, Kubernetes (EKS/AKS), Terraform, CI/CD, automation, and production deployment. I apply these engineering practices to ML pipelines, model serving, LLM inference, and AI application infrastructure. Core Skills: Python | PyTorch | GenAI | LLMs | RAG | Agentic AI | LangChain | LangGraph | FastAPI | MLflow | Vector Databases | AWS | Azure | Docker | Kubernetes | Terraform | CI/CD | AI Observability | Model Monitoring | AI Evaluation I'm passionate about solving real-world AI engineering challenges—from deploying models and optimizing inference to evaluating AI outputs, improving agent reliability, and scaling AI applications beyond proof of concept. I'm open to opportunities in AI/ML Engineering, GenAI/LLM Engineering, MLOps, and AI Platform Engineering. Let's connect if you're building scalable AI products, intelligent agents, or production-grade ML platforms.

Location

Greater Bengaluru Area

AI stack

claude code

gemini

geminicli

github copilot

langchain

langgraph

Work experience

AI Engineer at KPMG India, Jul 2026 - Present · 4 mos, Bengaluru, Karnataka, India, AI/ML, GenAI & Agentic AI: Worked with PyTorch, TensorFlow, RAG pipelines, LLMs, local LLMs, AI agents, and agentic workflows, including model experimentation, deployment, inference pipelines, tool integrations, and AI application components such as speech-to-text and text-to-speech. MLOps & Cloud Engineering: Built and scaled production-grade ML/AI infrastructure across AWS and Azure, leveraging Terraform, Kubernetes (EKS/AKS), Docker, CI/CD, and cloud-native services with a focus on automation, reliability, scalability, and cost optimization. Enterprise AI Agent Platform: Contributed to building an enterprise AI Agent platform for agent lifecycle management, including agent registration, evaluation, governance, monitoring, and observability, enabling teams to assess and operate AI agents more reliably in production environments. AI/ML Observability & Governance: Worked on MLOps and AI observability platforms covering model and agent monitoring, evaluation, drift detection, performance tracking, governance, inference reliability, and operational insights to support continuous improvement of production AI systems.

MLOps Engineer at CGI, Jan 2026 - Jul 2026 · 7 mos, Bengaluru, Karnataka, India, Built and deployed end-to-end MLOps and LLMOps pipelines on Azure using Python, FastAPI, Docker, Kubernetes, MLflow, and CI/CD. Developed Retrieval-Augmented Generation (RAG) applications with document ingestion, chunking, embeddings, vector search, and LLM-based question answering. Automated model training, versioning, deployment, monitoring, and infrastructure provisioning using GitHub Actions, Azure DevOps, and Terraform. Implemented AI observability, security, RBAC, prompt guardrails, and performance monitoring for enterprise AI solutions.

DevOps Engineer at Tata Consultancy Services, Mar 2022 - Sep 2025 · 3 yrs 7 mos, Hyderabad, Telangana, India, - Designed, deployed, and managed scalable cloud infrastructure across AWS and Azure using EC2, S3, EKS, AKS, VPC, IAM, VNets, Azure Storage, and cloud-native services. - Automated CI/CD pipelines using GitHub Actions, Azure DevOps, and Jenkins, integrating automated testing, security scanning, Docker builds, and Kubernetes deployments. - Implemented Infrastructure as Code and container orchestration using Terraform, Ansible, Docker, and Kubernetes (EKS/AKS), supporting reliable multi-environment deployments. - Managed monitoring, observability, and MLOps workloads using CloudWatch, Azure Monitor, Prometheus, Grafana, and MLflow, with hands-on deployment of FastAPI-based ML/AI services.

Education

Bachelor of Technology - BTech, Guru Nanak Institutions(GNI)