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I’m a Machine Learning Engineer who believes that trust in AI begins with authenticity in the people who build it. My work blends clear thinking, deep technical craft, and an instinct for meaningful impact. At Goldman Sachs, I design GenAI copilots and multi-agent reasoning systems that enhance financial research and compliance with explainable, reliable intelligence. My experience spans hybrid RAG pipelines, transformer fine-tuning (BERT, T5, LoRA/QLoRA), and real-time observability using Grafana, W&B, and TruLens all grounded in scalable MLOps practices across AWS, FastAPI, Ray, and Docker. Previously at Accenture, I delivered enterprise ML pipelines that automated workflows and reduced operational inefficiencies across retail, healthcare, and finance. What drives me is building AI systems that people can understand, trust, and depend on solutions that turn complexity into clarity and deliver real, measurable value with integrity.…I’m a Machine Learning Engineer who believes that trust in AI begins with authenticity in the people who build it. My work blends clear thinking, deep technical craft, and an instinct for meaningful impact. At Goldman Sachs, I design GenAI copilots and multi-agent reasoning systems that enhance financial research and compliance with explainable, reliable intelligence. My experience spans hybrid RAG pipelines, transformer fine-tuning (BERT, T5, LoRA/QLoRA), and real-time observability using Grafana, W&B, and TruLens all grounded in scalable MLOps practices across AWS, FastAPI, Ray, and Docker. Previously at Accenture, I delivered enterprise ML pipelines that automated workflows and reduced operational inefficiencies across retail, healthcare, and finance. What drives me is building AI systems that people can understand, trust, and depend on solutions that turn complexity into clarity and deliver real, measurable value with integrity.

Rathan Devulapally

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

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I’m a Machine Learning Engineer who believes that trust in AI begins with authenticity in the people who build it. My work blends clear thinking, deep technical craft, and an instinct for meaningful impact. At Goldman Sachs, I design GenAI copilots and multi-agent reasoning systems that enhance financial research and compliance with explainable, reliable intelligence. My experience spans hybrid RAG pipelines, transformer fine-tuning (BERT, T5, LoRA/QLoRA), and real-time observability using Grafana, W&B, and TruLens all grounded in scalable MLOps practices across AWS, FastAPI, Ray, and Docker. Previously at Accenture, I delivered enterprise ML pipelines that automated workflows and reduced operational inefficiencies across retail, healthcare, and finance. What drives me is building AI systems that people can understand, trust, and depend on solutions that turn complexity into clarity and deliver real, measurable value with integrity.…I’m a Machine Learning Engineer who believes that trust in AI begins with authenticity in the people who build it. My work blends clear thinking, deep technical craft, and an instinct for meaningful impact. At Goldman Sachs, I design GenAI copilots and multi-agent reasoning systems that enhance financial research and compliance with explainable, reliable intelligence. My experience spans hybrid RAG pipelines, transformer fine-tuning (BERT, T5, LoRA/QLoRA), and real-time observability using Grafana, W&B, and TruLens all grounded in scalable MLOps practices across AWS, FastAPI, Ray, and Docker. Previously at Accenture, I delivered enterprise ML pipelines that automated workflows and reduced operational inefficiencies across retail, healthcare, and finance. What drives me is building AI systems that people can understand, trust, and depend on solutions that turn complexity into clarity and deliver real, measurable value with integrity.

Available to hire

I’m a Machine Learning Engineer who believes that trust in AI begins with authenticity in the people who build it. My work blends clear thinking, deep technical craft, and an instinct for meaningful impact.

At Goldman Sachs, I design GenAI copilots and multi-agent reasoning systems that enhance financial research and compliance with explainable, reliable intelligence. My experience spans hybrid RAG pipelines, transformer fine-tuning (BERT, T5, LoRA/QLoRA), and real-time observability using Grafana, W&B, and TruLens all grounded in scalable MLOps practices across AWS, FastAPI, Ray, and Docker. Previously at Accenture, I delivered enterprise ML pipelines that automated workflows and reduced operational inefficiencies across retail, healthcare, and finance. What drives me is building AI systems that people can understand, trust, and depend on solutions that turn complexity into clarity and deliver real, measurable value with integrity.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

Machine Learning Engineer at Bank Of America

August 1, 2024 - Present

Contributed to hybrid retrieval augmented generation (RAG) pipeline design using FAISS and Qdrant for internal research and compliance, boosting retrieval precision by 30% and reducing irrelevant hits by 25%. Fine-tuned BERT and T5 models with LoRA/QLoRA to improve domain understanding by 22% and cut manual validation by 40%. Built real-time text-to-SQL and reasoning agents with FastAPI, Ray, and Docker, delivering 2.3x faster query responses. Strengthened reliability with Redis caching, AWS Cognito authentication, and PromptLayer tracing to improve stability by 35% and ensure complete audit traceability. Implemented Grafana, Weights & Biases, and TruLens-based monitoring for continuous prompt evaluation, reducing debugging cycles by 45%. Collaborated across ML, data science, and compliance to sharpen agent objectives and context strategies, increasing decision accuracy by 28% and throughput by 15%.

Machine Learning Engineer at Accenture

September 1, 2021 - May 1, 2023

Delivered 12+ end-to-end ML pipelines across retail, healthcare, and finance, automating model workflows with Python, PySpark, and AWS (S3, Glue, SageMaker) to reduce deployment time by 25% and infrastructure costs by 20%. Accelerated CNN and Transformer training by 30% via distributed model training using Horovod and Ray on Spark clusters. Built automated CI/CD pipelines with MLflow, Jenkins, and Docker to improve reproducibility and release reliability. Created real-time observability dashboards with Power BI and AWS CloudWatch to monitor drift, latency, and system health, cutting issue resolution time by 35%. Contributed to secure, compliant AWS architectures through Well-Architected Framework reviews and IAM governance for scalable ML infrastructure.