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I am an AI/ML Engineer with 4+ years of experience delivering scalable ML solutions in finance and healthcare, building automated pipelines, and solving data-driven challenges. I enjoy turning data into actionable insights and building robust ML systems. I thrive in collaborative environments and relish translating research into production-grade solutions that drive tangible business impact. I have strong MLOps and model governance experience, optimizing performance for high-reliability environments. I am proficient with Python, TensorFlow, PyTorch, and cloud-native stacks (AWS/GCP/Azure), and I routinely work on end-to-end pipelines, model evaluation, and governance to ensure compliant and auditable AI systems. My strengths include cross-functional collaboration, scalable architecture design, and continuous learning to keep pace with rapid AI advances.…I am an AI/ML Engineer with 4+ years of experience delivering scalable ML solutions in finance and healthcare, building automated pipelines, and solving data-driven challenges. I enjoy turning data into actionable insights and building robust ML systems. I thrive in collaborative environments and relish translating research into production-grade solutions that drive tangible business impact. I have strong MLOps and model governance experience, optimizing performance for high-reliability environments. I am proficient with Python, TensorFlow, PyTorch, and cloud-native stacks (AWS/GCP/Azure), and I routinely work on end-to-end pipelines, model evaluation, and governance to ensure compliant and auditable AI systems. My strengths include cross-functional collaboration, scalable architecture design, and continuous learning to keep pace with rapid AI advances.

SAI SIVA SRI HARSHA VASINENKU

AI Engineer, Data Scientist, Full Stack Developer, +3

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I am an AI/ML Engineer with 4+ years of experience delivering scalable ML solutions in finance and healthcare, building automated pipelines, and solving data-driven challenges. I enjoy turning data into actionable insights and building robust ML systems. I thrive in collaborative environments and relish translating research into production-grade solutions that drive tangible business impact. I have strong MLOps and model governance experience, optimizing performance for high-reliability environments. I am proficient with Python, TensorFlow, PyTorch, and cloud-native stacks (AWS/GCP/Azure), and I routinely work on end-to-end pipelines, model evaluation, and governance to ensure compliant and auditable AI systems. My strengths include cross-functional collaboration, scalable architecture design, and continuous learning to keep pace with rapid AI advances.…I am an AI/ML Engineer with 4+ years of experience delivering scalable ML solutions in finance and healthcare, building automated pipelines, and solving data-driven challenges. I enjoy turning data into actionable insights and building robust ML systems. I thrive in collaborative environments and relish translating research into production-grade solutions that drive tangible business impact. I have strong MLOps and model governance experience, optimizing performance for high-reliability environments. I am proficient with Python, TensorFlow, PyTorch, and cloud-native stacks (AWS/GCP/Azure), and I routinely work on end-to-end pipelines, model evaluation, and governance to ensure compliant and auditable AI systems. My strengths include cross-functional collaboration, scalable architecture design, and continuous learning to keep pace with rapid AI advances.

Available to hire

I am an AI/ML Engineer with 4+ years of experience delivering scalable ML solutions in finance and healthcare, building automated pipelines, and solving data-driven challenges. I enjoy turning data into actionable insights and building robust ML systems. I thrive in collaborative environments and relish translating research into production-grade solutions that drive tangible business impact.

I have strong MLOps and model governance experience, optimizing performance for high-reliability environments. I am proficient with Python, TensorFlow, PyTorch, and cloud-native stacks (AWS/GCP/Azure), and I routinely work on end-to-end pipelines, model evaluation, and governance to ensure compliant and auditable AI systems. My strengths include cross-functional collaboration, scalable architecture design, and continuous learning to keep pace with rapid AI advances.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Work Experience

AI/ML Engineer at Fidelity Investments

August 1, 2024 - Present

Designed modular Generative AI workflows combining LLMs, vector search, and structured financial data pipelines to deliver personalized wealth insights, automated portfolio summaries, and real-time market intelligence across advisory platforms. Built LangChain LLaMA pipelines analyzing 12M financial reports, commentaries, and advisor notes, improving research retrieval and advisory decision-support accuracy by 14%, enabling faster data-driven wealth-management decisions. Implemented production-grade MLOps pipelines with Kubeflow, Kubernetes, and Azure DevOps, managing 28 concurrent LLM microservices with 99.4% uptime and standardized CI/CD and monitoring for financial-use-case LLMs. Trained domain-specific financial NLP models in Azure ML using Hugging Face Transformers to extract market sentiment, earnings signals, and risk indicators from 1,600,000 filings and research notes (F1 0.87). Built secure LLM fine-tuning and prompt-management pipelines using Prompt Engineering, LangGraph, a

AI/ML Engineer at Capgemini India

September 1, 2020 - July 1, 2023

Led modernization of hospital imaging workflows by integrating large-scale chest X-ray datasets into automated ML pipelines, improving pneumonia screening efficiency by 21% and reducing radiologist diagnostic turnaround time by 18% across multi-hospital imaging systems. Developed deep learning–based pneumonia detection models using PyTorch, training on 120K+ annotated chest X-ray images, improving diagnostic accuracy by 16% and decreasing manual radiologist review workload by 20%. Built ensemble diagnostic models using XGBoost and Scikit-Learn, applying SMOTE-based class balancing to improve rare pneumonia subtype detection, increasing precision by 17% and improving recall for minority classes by 14%. Designed scalable data ingestion and preprocessing pipelines using Apache Spark and Kafka, processing 40K+ imaging records daily, reducing data preparation time by 35% and improving training dataset availability for ML workflows. Automated model training and data orchestration workflows