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I am a results-driven AI/ML engineer with over 10 years of experience designing and deploying intelligent, agentic, and retrieval-augmented systems in large-scale enterprise environments. I specialize in GenAI and multi-agent orchestration, delivering end-to-end platforms using cutting-edge LLM technologies and orchestration frameworks. I excel in prompt engineering, embeddings optimization, RAG pipelines, and production-grade deployments on cloud platforms. I enjoy mentoring teams, collaborating across data science, ML engineering, and DevOps, and aligning AI solutions with business goals while ensuring governance, security, and ethical use of GenAI.…I am a results-driven AI/ML engineer with over 10 years of experience designing and deploying intelligent, agentic, and retrieval-augmented systems in large-scale enterprise environments. I specialize in GenAI and multi-agent orchestration, delivering end-to-end platforms using cutting-edge LLM technologies and orchestration frameworks. I excel in prompt engineering, embeddings optimization, RAG pipelines, and production-grade deployments on cloud platforms. I enjoy mentoring teams, collaborating across data science, ML engineering, and DevOps, and aligning AI solutions with business goals while ensuring governance, security, and ethical use of GenAI.

Praneeth Chandra Suraboina

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

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I am a results-driven AI/ML engineer with over 10 years of experience designing and deploying intelligent, agentic, and retrieval-augmented systems in large-scale enterprise environments. I specialize in GenAI and multi-agent orchestration, delivering end-to-end platforms using cutting-edge LLM technologies and orchestration frameworks. I excel in prompt engineering, embeddings optimization, RAG pipelines, and production-grade deployments on cloud platforms. I enjoy mentoring teams, collaborating across data science, ML engineering, and DevOps, and aligning AI solutions with business goals while ensuring governance, security, and ethical use of GenAI.…I am a results-driven AI/ML engineer with over 10 years of experience designing and deploying intelligent, agentic, and retrieval-augmented systems in large-scale enterprise environments. I specialize in GenAI and multi-agent orchestration, delivering end-to-end platforms using cutting-edge LLM technologies and orchestration frameworks. I excel in prompt engineering, embeddings optimization, RAG pipelines, and production-grade deployments on cloud platforms. I enjoy mentoring teams, collaborating across data science, ML engineering, and DevOps, and aligning AI solutions with business goals while ensuring governance, security, and ethical use of GenAI.

Available to hire

I am a results-driven AI/ML engineer with over 10 years of experience designing and deploying intelligent, agentic, and retrieval-augmented systems in large-scale enterprise environments. I specialize in GenAI and multi-agent orchestration, delivering end-to-end platforms using cutting-edge LLM technologies and orchestration frameworks.

I excel in prompt engineering, embeddings optimization, RAG pipelines, and production-grade deployments on cloud platforms. I enjoy mentoring teams, collaborating across data science, ML engineering, and DevOps, and aligning AI solutions with business goals while ensuring governance, security, and ethical use of GenAI.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Senior GenAI/ML Engineer at Mastercard

September 1, 2024 - Present

Designed GenAI-powered copilots embedded within merchandising platforms to enable product comparison, pricing rules, and supplier resolution using AI-driven insights. Architected LLM-based chatbot modules with Azure AI Foundry, Azure Cognitive Services, and Semantic Kernel, integrated with Urban Science’s MarketView platform. Built retrieval-augmented chat agents using FAISS and LangChain retrievers for contextual, domain-aware responses. Implemented MCP pipelines for multi-turn conversations and context persistence; integrated LLM services into .NET Core Web APIs and Angular frontends via REST and SignalR for low-latency interactions. Evaluated and fine-tuned models (GPT-4, Claude 3, LLaMA 3). Deployed open-source LLMs in private enterprise environments and CPU-optimized inference. Implemented metadata-based filtering in vector retrieval to enforce role-based access during RAG. Created on-prem reference architectures for LLM + vector DB deployments. Authored runbooks and training ma

AI / ML Engineer at JPMorgan

October 1, 2022 - August 31, 2024

Developed LangChain- and Semantic Kernel-based autonomous agents for real-time risk assessment, market summaries, and data querying. Trained Claude 3 and GPT-4 with LoRA and SFT on proprietary financial data to produce institution-specific GenAI outputs. Engineered time-series forecasting into agent workflows for risk and market monitoring; built cost-optimized prompt routing across Claude, GPT-4o, and Mistral; integrated Qdrant/pgvector-based embeddings and retrieval. Created Python-based APIs (FastAPI, REST, GraphQL); deployed GenAI microservices on Kubernetes with Terraform; established observability with Prometheus and Grafana; implemented HIPAA-compliant data handling and RBAC. Built internal governance, sandbox environments, and fallback mechanisms; delivered dashboards in Power BI; connected LangChain agents to Snowflake, PostgreSQL, and ServiceNow to automate processes; ensured CI/CD via GitHub Actions; managed multi-service orchestration with Kubernetes and vLLM inference.

Data Scientist / ML Engineer at State Of Connecticut, Hartford

February 1, 2020 - September 30, 2022

Led data engineering and ML initiatives to support regulatory analytics. Built private semantic search and retrieval over structured SQL data and vector embeddings; designed early RAG workflows for document discovery across government datasets. Implemented end-to-end ETL pipelines, Snowflake schemas, and data marts; automated deployments with Docker and AWS SageMaker; performed data validation and drift checks; built ML models (XGBoost, Random Forest, SVM) for risk scoring and credit analysis. Created multilingual pipelines and dashboards in Power BI; ensured data residency and on-prem data handling; contributed to data governance and reporting for state stakeholders.

Data Scientist at Target

May 1, 2017 - December 31, 2019

Designed predictive analytics models to forecast sales demand, inventory needs, and customer purchase behavior, improving operational planning. Developed recommendation engines using collaborative filtering and transformer-based models, increasing personalization in retail promotions. Implemented churn prediction models, NLP pipelines for product categorization and review sentiment, and fraud detection. Created pricing optimization frameworks with regression and reinforcement learning; automated data preprocessing with Apache Spark and Python; deployed on AWS/Azure; partnered with UX/UI teams to integrate AI-driven insights into web and mobile applications; conducted A/B testing and built dashboards in Tableau and Power BI.

Data Analyst – Process Associate at T-Mobile

June 1, 2016 - May 31, 2017

Built customer churn prediction models using Logistic Regression, Random Forest, and XGBoost; implemented network anomaly detection with Isolation Forest and Autoencoders; conducted sentiment analysis of support interactions. Built ETL and streaming pipelines with Apache Kafka and Spark for real-time telecom analytics; automated customer segmentation; collaborated to improve ETL processes for ML readiness. Orchestrated multi-cluster ML workloads (MCP) across EKS for high-availability inference and parallelized data processing; implemented monitoring and drift detection for production pipelines.

Data Analyst – Process Associate at American Airlines

March 1, 2014 - May 31, 2016

Created SQL-based risk analysis reports supporting credit risk evaluations; designed ETL workflows with Alteryx and Python to ensure data quality. Built dynamic Power BI dashboards for real-time portfolio analytics and credit monitoring; performed data imputation and feature engineering; automated ad-hoc reporting with SQL. Collaborated with BI teams to enhance data visualization practices and ensure data integrity for audits.