Suvidha Sharma
Suvidha Sharma

AI/ML Engineer

Actively looking · Member since 29 Oct 2026
Location
New Bedford, United States
Desired salary
Unspecified
Work preference
Remote Only
Experience level
Senior

About

Professional summary

I am an AI/ML Engineer with over four years of experience building production-grade machine learning, Generative AI, and Agentic AI solutions for financial services and healthcare organizations.

I specialize in enterprise RAG and GraphRAG systems, LLM applications, multi-agent workflows, fine-tuning, model evaluation, and predictive modeling. I build solutions that improve research, portfolio analytics, fraud detection, clinical decision support, and operational intelligence.

I have hands-on experience delivering cloud-native AI platforms across Azure, AWS, and Google Cloud. My work includes deploying scalable inference services, APIs, microservices, and real-time data pipelines using containers and Kubernetes.

I am experienced in MLOps and LLMOps practices, including experiment tracking, model registries, CI/CD, monitoring, drift detection, evaluation frameworks, and production governance. I prioritize reliable, measurable, and maintainable AI systems.

I work closely with business stakeholders, engineers, analysts, clinicians, and compliance teams to translate complex requirements into secure AI products. I also focus on Responsible AI, model risk management, privacy, access controls, and auditability.

Skills

30 capabilities

Tech stack & tools

Working toolkit

Experience

Career history

AI/ML Engineer State Street

Architected an Agentic AI platform using Python and LangGraph with more than 12 autonomous agents, reducing manual analyst effort by 40% across investment research and portfolio analytics. Built a GraphRAG knowledge retrieval platform with Neo4j and LlamaIndex across more than 500,000 financial documents, improving retrieval relevance by 32%.

Fine-tuned domain-specific LLMs using PEFT and LoRA, improving response accuracy by 22% while reducing hallucinations by 30%. Developed evaluation and monitoring with LangSmith and RAGAS, predictive risk models using XGBoost and PyTorch, and AML and trade-surveillance pipelines with PySpark and Databricks.

Implemented MLOps and LLMOps workflows with MLflow and GitHub Actions, deployed inference services with Docker and Kubernetes, and introduced governance controls including audit logging, RBAC, data lineage, and Responsible AI practices. Collaborated with portfolio managers, quantitative analysts, compliance teams, and engineers to deliver production AI solutions.

AI/ML Engineer IBM

Developed end-to-end machine learning and Generative AI solutions using Python and Google Vertex AI for healthcare analytics, clinical decision support, and operational improvement. Integrated FHIR and HL7 standards with EMR/EHR systems across more than 15 hospital networks, reducing healthcare data mapping and integration errors by 20%.

Built Clinical NLP and named-entity recognition pipelines using ClinicalBERT and spaCy to process more than 200,000 physician notes, reducing manual chart-review time by 30%. Fine-tuned healthcare LLMs with PEFT, LoRA, and RAG to improve clinical summarization, medical information retrieval, and documentation efficiency.

Created disease-risk and patient-outcome models using XGBoost and TensorFlow, applying SHAP to improve transparency for clinical users. Built MLOps pipelines with MLflow, Docker, and Kubernetes, and developed LangChain and FastAPI healthcare assistants supporting patient triage, information retrieval, and appointment assistance across more than 10,000 monthly interactions.

Education

Learning history

University of Massachusetts Dartmouth

Master of Data Science, Data Science

Master of Data Science.

Guru Nanak Dev Engineering College

Bachelor of Information Technology, Information Technology

Bachelor of Information Technology.

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