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AI/Machine Learning Engineer with 4+ years of experience building scalable Generative AI, applied machine learning, and cloud-native AI platforms across enterprise SaaS, retail, and knowledge automation domains. Experienced in production-grade RAG pipelines, recommendation systems, predictive analytics, and MLOps infrastructure using Python, TensorFlow, GPT-4o, LangChain, LlamaIndex, FastAPI, and AWS, with strong focus on secure, high-performance deployments and measurable business impact at scale.…AI/Machine Learning Engineer with 4+ years of experience building scalable Generative AI, applied machine learning, and cloud-native AI platforms across enterprise SaaS, retail, and knowledge automation domains. Experienced in production-grade RAG pipelines, recommendation systems, predictive analytics, and MLOps infrastructure using Python, TensorFlow, GPT-4o, LangChain, LlamaIndex, FastAPI, and AWS, with strong focus on secure, high-performance deployments and measurable business impact at scale.

Dharani Theja Kanjeri

Data Scientist, AI Developer, Developer, +5

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AI/Machine Learning Engineer with 4+ years of experience building scalable Generative AI, applied machine learning, and cloud-native AI platforms across enterprise SaaS, retail, and knowledge automation domains. Experienced in production-grade RAG pipelines, recommendation systems, predictive analytics, and MLOps infrastructure using Python, TensorFlow, GPT-4o, LangChain, LlamaIndex, FastAPI, and AWS, with strong focus on secure, high-performance deployments and measurable business impact at scale.…AI/Machine Learning Engineer with 4+ years of experience building scalable Generative AI, applied machine learning, and cloud-native AI platforms across enterprise SaaS, retail, and knowledge automation domains. Experienced in production-grade RAG pipelines, recommendation systems, predictive analytics, and MLOps infrastructure using Python, TensorFlow, GPT-4o, LangChain, LlamaIndex, FastAPI, and AWS, with strong focus on secure, high-performance deployments and measurable business impact at scale.

Available to hire

AI/Machine Learning Engineer with 4+ years of experience building scalable Generative AI, applied machine learning, and cloud-native AI platforms across enterprise SaaS, retail, and knowledge automation domains.
Experienced in production-grade RAG pipelines, recommendation systems, predictive analytics, and MLOps infrastructure using Python, TensorFlow, GPT-4o, LangChain, LlamaIndex, FastAPI, and AWS, with strong focus on secure, high-performance deployments and measurable business impact at scale.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI/Machine Learning Engineer at Glean

May 1, 2025 - Present

Engineered production-grade RAG pipelines using GPT-4o, LangChain, LlamaIndex, vector databases, and hybrid retrieval architectures to reduce hallucinations across 2M+ daily enterprise interactions for 5 clients. Architected real-time recommendation systems with TensorFlow, AWS SageMaker, Apache Kafka, Redis, and Feast, delivering 50K+ requests/sec with sub-120ms P99 latency. Led 6 concurrent ML experiments for personalized ranking, boosting revenue per session by 38% within 90 days. Streamlined end-to-end MLOps for a 12-member team using MLflow, Airflow, Docker, Kubernetes, and GitHub Actions, cutting deployment cycles from 3 days to 4 hours. Developed predictive analytics platforms (XGBoost churn, LSTM demand forecasting, Isolation Forest) enabling faster planning. Optimized GenAI inference with ONNX Runtime and INT8 quantization, reducing costs by 31% and increasing throughput. Designed multi-tenant gRPC/REST APIs with FastAPI, OAuth 2.0, RBAC, and rate limiting for secure enterpris

AI/Machine Learning Engineer at Freshworks

July 1, 2021 - June 30, 2024

Led end-to-end development of an enterprise GenAI knowledge assistant across the full SDLC using Python, FastAPI, React, OpenAI APIs, and AWS, enabling 15k+ employees to retrieve internal knowledge 75% faster across six units. Architected microservices on AWS EKS, Lambda, S3, API Gateway, and PostgreSQL, aligning AI solutions with enterprise requirements. Engineered large-scale document ingestion pipelines (Airflow, OCR, PyMuPDF, AWS S3) indexing 2M+ documents into a vectorized knowledge base for real-time retrieval. Built hybrid RAG pipelines with LangChain, OpenAI Embeddings, Pinecone, BM25, and cross-encoder reranking, improving answer relevance by 41% and achieving 0.91 faithfulness. Reduced LLM API costs by 45% via Redis caching, prompt compression, query routing, and token optimization. Developed secure conversational AI workflows with JWT, RBAC, audit logging, and role-based document permissions across 6 units with zero security incidents. Scaled reliability with Docker, Kuberne