Anjali Banthia

AI Engineer | Machine Learning | Data Engineering | LLMs & RAG Pipelines | Intelligent Agents

Talent

BīkanerMember since July 28, 2026

LangChainOpenAI APIGemini APIPrompt EngineeringRAG PipelinesIntelligent AgentsAWS BedrockGCP Vertex AIPyTorchTensorFlowNLPScikit-learnFeature EngineeringEDAPineconeChromaDBPostgreSQLMySQLSQLFastAPISpring BootDockerREST APIsCI/CDMicroservicesPythonJavaJavaScriptCollaborative Filtering

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LangChainOpenAI APIGemini APIPrompt EngineeringRAG Pipelines

About

Results-driven AI/ML Engineer with 1.5+ years of production experience designing and deploying Generative AI solutions, LLM-integrated backends, and end-to-end RAG pipelines. Skilled in OpenAI API, Gemini API, LangChain, prompt engineering, intelligent agent orchestration, AWS Bedrock, and GCP Vertex AI. Proficient in Python, FastAPI, Docker, and cloud-native data pipelines.

AWS Machine Learning Specialist and Microsoft Generative AI certified. GATE DA 2026 qualified (AIR 3863). Adept at full-cycle AI development—from research and EDA to scalable cloud deployment—in Agile/Scrum, fully remote environments.

Experience

Software Developer Intern - Emizentech Pvt Ltd - Jaipur, India

(2025-01 - 2025-06)

Designed and deployed RESTful API backends as integration layers for AI-powered microservices and LLM data pipelines (Python, JavaScript, MySQL, Docker) within an Agile/Scrum delivery cycle.
Architected modular, scalable backend services with zero production incidents; implemented LLM integration and AI service orchestration patterns aligned with cloud deployment standards.
Engineered JWT-based authentication middleware; containerised services via Docker for reproducible, cloud-ready deployments; participated in sprint planning and stakeholder demos.

AI / ML Research Intern - IBM - Remote, India

(2024-06 - 2024-08)

Built cloud-native AI data pipelines integrating neural network inference models on AWS; processed 100,000+ record medical datasets (Python, TensorFlow, REST APIs) achieving 91% F1-score on clinical classification tasks.
Engineered advanced LLM prompt strategies for clinical NLP; performed EDA and feature engineering; implemented Generative AI for automated multi-document summarisation on distributed datasets.
Designed and deployed ML models (PyTorch, Scikit-learn) for real-time inference via cloud REST APIs; collaborated with senior researchers and cross-functional stakeholders in a fully remote setup.