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AI Engineer with 4+ years of end-to-end experience building production generative AI, agentic workflows, and MLOps systems across enterprise environments (Uber, CGI India, Product Manager Accelerator). I design LLM/RAG pipelines, fine-tune models, and ship full-stack AI features from scoping through deployment while maintaining strong quality gates. I focus on building reliable, low-latency AI services using FastAPI, React, and cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI), and I improve outcomes through better retrieval precision, prompt/agent workflows, systematic evaluation, hyperparameter tuning, and monitoring for drift and performance over time.…AI Engineer with 4+ years of end-to-end experience building production generative AI, agentic workflows, and MLOps systems across enterprise environments (Uber, CGI India, Product Manager Accelerator). I design LLM/RAG pipelines, fine-tune models, and ship full-stack AI features from scoping through deployment while maintaining strong quality gates. I focus on building reliable, low-latency AI services using FastAPI, React, and cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI), and I improve outcomes through better retrieval precision, prompt/agent workflows, systematic evaluation, hyperparameter tuning, and monitoring for drift and performance over time.

Shivayokeshwari Athappan

AI Engineer, Full Stack Developer, Data Scientist, +2

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AI Engineer with 4+ years of end-to-end experience building production generative AI, agentic workflows, and MLOps systems across enterprise environments (Uber, CGI India, Product Manager Accelerator). I design LLM/RAG pipelines, fine-tune models, and ship full-stack AI features from scoping through deployment while maintaining strong quality gates. I focus on building reliable, low-latency AI services using FastAPI, React, and cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI), and I improve outcomes through better retrieval precision, prompt/agent workflows, systematic evaluation, hyperparameter tuning, and monitoring for drift and performance over time.…AI Engineer with 4+ years of end-to-end experience building production generative AI, agentic workflows, and MLOps systems across enterprise environments (Uber, CGI India, Product Manager Accelerator). I design LLM/RAG pipelines, fine-tune models, and ship full-stack AI features from scoping through deployment while maintaining strong quality gates. I focus on building reliable, low-latency AI services using FastAPI, React, and cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI), and I improve outcomes through better retrieval precision, prompt/agent workflows, systematic evaluation, hyperparameter tuning, and monitoring for drift and performance over time.

Available to hire

AI Engineer with 4+ years of end-to-end experience building production generative AI, agentic workflows, and MLOps systems across enterprise environments (Uber, CGI India, Product Manager Accelerator). I design LLM/RAG pipelines, fine-tune models, and ship full-stack AI features from scoping through deployment while maintaining strong quality gates.

I focus on building reliable, low-latency AI services using FastAPI, React, and cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI), and I improve outcomes through better retrieval precision, prompt/agent workflows, systematic evaluation, hyperparameter tuning, and monitoring for drift and performance over time.

Skills

Experience Level

Expert

Expert

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Intermediate

Language

Work Experience

AI Engineer at Uber

January 1, 2026 - Present

Designed and developed production-ready generative AI and agentic applications integrating multimodal foundation models, structured output validation, and business logic layers. Implemented RAG pipelines using LangChain with FAISS and Pinecone to ground responses in proprietary knowledge bases, improving relevance and reducing hallucinations. Built multi-step agentic workflows with tool-use logic and memory management using LangChain and AutoGen, reducing human-in-the-loop intervention. Developed MCP integrations to inject accurate real-time enterprise context and added async React + FastAPI endpoints for inference, retrieval, and agent workflows. Deployed and monitored workloads across AWS SageMaker, Azure ML, and GCP Vertex AI, achieving sub-200ms response times. Contributed to ML CI/CD automation with GitHub Actions, Docker containerization, and cloud-native monitoring, reducing manual release effort and enforcing quality gates.

AI Engineer at CGI India

January 1, 2021 - June 30, 2024

Engineered and deployed enterprise ML and GenAI applications integrating GPT/LLaMA, prompt engineering workflows, and external APIs (REST/GraphQL/gRPC), reducing manual task handling and enabling AI-assisted capabilities. Built supervised ML models for classification, regression, clustering, and time-series forecasting using Scikit-learn, XGBoost/LightGBM, and PyTorch/TensorFlow, improving accuracy via hyperparameter tuning and feature engineering. Implemented RAG pipelines with LangChain, FAISS, and Pinecone to improve response relevance and reduce off-topic answers. Developed LSTM-based time-series forecasting models to reduce forecast variance and replace manual estimation workflows. Added async React front-end integration and implemented end-to-end data pipelines (ingestion to monitoring), reducing pipeline failure rates. Containerized services and supported Kubernetes deployments while monitoring drift and triggering automated retraining to sustain performance over rolling windows

AI Engineer at Product Manager Accelerator

August 1, 2020 - December 31, 2020

Collaborated with product managers, UX designers, and data scientists to translate product requirements into technical implementation specs, reducing engineering/design misalignment and iteration cycles. Engineered and deployed production-ready GenAI applications with LLM prompting and ML inference back-ends, delivering sub-200ms responses under production load. Built REST/GraphQL/gRPC service layers and optimized integration latency via payload optimization and connection pooling. Implemented post-deployment monitoring for drift and latency degradation with automated retraining cycles. Evaluated and prototyped multiple LLM integration patterns to inform toolchain adoption while maintaining sprint velocity above 95%.