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AI/ML Engineer with 4+ years of experience building production AI systems, large-scale backend platforms, and intelligent data pipelines serving millions of users. Experienced in LLM applications (including RAG), retrieval systems, recommendation/ranking, semantic search, and distributed cloud-native AI infrastructure. Proven track record of improving retrieval quality, reducing cloud costs, optimizing production inference, and delivering scalable AI-powered products for both consumer and enterprise applications across multiple clouds and production environments.…AI/ML Engineer with 4+ years of experience building production AI systems, large-scale backend platforms, and intelligent data pipelines serving millions of users. Experienced in LLM applications (including RAG), retrieval systems, recommendation/ranking, semantic search, and distributed cloud-native AI infrastructure. Proven track record of improving retrieval quality, reducing cloud costs, optimizing production inference, and delivering scalable AI-powered products for both consumer and enterprise applications across multiple clouds and production environments.

Yamuna Deepika Yarlagadda

AI Engineer, Data Engineer, Software Engineer, +3

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AI/ML Engineer with 4+ years of experience building production AI systems, large-scale backend platforms, and intelligent data pipelines serving millions of users. Experienced in LLM applications (including RAG), retrieval systems, recommendation/ranking, semantic search, and distributed cloud-native AI infrastructure. Proven track record of improving retrieval quality, reducing cloud costs, optimizing production inference, and delivering scalable AI-powered products for both consumer and enterprise applications across multiple clouds and production environments.…AI/ML Engineer with 4+ years of experience building production AI systems, large-scale backend platforms, and intelligent data pipelines serving millions of users. Experienced in LLM applications (including RAG), retrieval systems, recommendation/ranking, semantic search, and distributed cloud-native AI infrastructure. Proven track record of improving retrieval quality, reducing cloud costs, optimizing production inference, and delivering scalable AI-powered products for both consumer and enterprise applications across multiple clouds and production environments.

Available to hire

AI/ML Engineer with 4+ years of experience building production AI systems, large-scale backend platforms, and intelligent data pipelines serving millions of users. Experienced in LLM applications (including RAG), retrieval systems, recommendation/ranking, semantic search, and distributed cloud-native AI infrastructure.

Proven track record of improving retrieval quality, reducing cloud costs, optimizing production inference, and delivering scalable AI-powered products for both consumer and enterprise applications across multiple clouds and production environments.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Beginner

Beginner

Work Experience

Senior Backend Engineer +1 at Phia

April 1, 2026 - July 31, 2026

Rebuilt the Digital Closet platform from scratch, designing and shipping scalable backend services that capture and organize users’ online purchases into a centralized digital wardrobe via browser extension integrations. Developed event-driven purchase ingestion pipelines for real-time detection and processing of newly completed purchases. Implemented Gmail integration using Gmail APIs, Watch API, OAuth 2.0, and Google Cloud Pub/Sub to support real-time synchronization and up to two years of historical backfill. Built Apache Airflow DAGs to automate daily migration of OpenSearch snapshots from Google Cloud Storage to AWS OpenSearch, reducing costs by approximately $20,000/month. Engineered automated partner discovery pipelines, including deduplication across affiliate networks and metadata quality selection workflows to improve data integrity and expand marketplace coverage. Owned backend services and pipelines using Python, Apache Airflow, BigQuery, Google Cloud Storage, Amazon S3,

AI/ML Engineer at Meta

June 1, 2025 - March 31, 2026

Built real-time personalization and discovery systems powering content ranking across feed and recommendation surfaces for large-scale audiences, improving relevance and engagement across sessions. Developed dual-encoder embedding models to improve engagement relevance metrics and candidate quality. Designed large-scale candidate retrieval pipelines using ANN indexing (HNSW, IVF, PQ) to reduce latency while preserving high recall under heavy traffic. Implemented multi-stage ranking combining retrieval, filtering, and re-ranking using behavioral and contextual interaction signals. Built LLM-powered RAG workflows with prompt orchestration, grounding checks, guardrails, and fallback logic. Applied parameter-efficient fine-tuning (LoRA/QLoRA/PEFT) on LLaMA and BART models to reduce GPU training and iteration cost. Designed low-latency production inference services (Python/C++) for embedding and ranking APIs used by multiple product surfaces. Developed evaluation pipelines including offline

AI/ML Engineer at Microsoft

August 1, 2020 - July 31, 2023

Contributed to an enterprise NLP and search platform supporting automation and knowledge retrieval across large structured and unstructured datasets used by internal business teams. Developed and fine-tuned BERT and transformer-based models for classification, entity extraction, sentiment analysis, and semantic similarity to improve routing accuracy and information discovery quality. Implemented semantic search components using embedding retrieval and FAISS indexing to reduce query response latency. Worked across the end-to-end ML lifecycle including ingestion, feature engineering, training, validation, deployment, monitoring, and retraining. Built cloud-native ML pipelines using Azure ML, ADLS, Data Factory, AKS, and Azure DevOps with Python-based orchestration and services. Developed production C++ backend modules for high-throughput compute-intensive ML workflows. Integrated experiment tracking, A/B testing, monitoring tooling, explainability/governance checks (SHAP, LIME, fairness