Vetted seaborn Professionals

“AI/ML engineer who has shipped production AI systems end-to-end, including an automated multi-channel (Gmail/WhatsApp/voice) candidate interviewing workflow and an enterprise RAG knowledge search platform. Demonstrates strong production rigor (monitoring, A/B tests, guardrails, schema validation, shadow testing) with quantified impact: ~60–70% reduction in interview evaluation time and ~20–30% relevance gains in RAG retrieval.”

Monish Sri Sai Devineni

Screened

Mid-level Machine Learning Engineer specializing in financial AI, NLP, and MLOps

Boca Raton, FL5y exp

“AI/ML engineer with experience at Accenture and Morgan Stanley, building production LLM systems (GPT-3 summarization) and finance-focused ML models (credit risk and trading anomaly detection). Combines MLOps depth (Docker/Kubernetes, AWS SageMaker/Glue/Lambda, MLflow, A/B testing, drift monitoring) with practical domain adaptation techniques like few-shot prompting and RAG/knowledge-base integration.”

Wei Jiang

Screened

Junior Machine Learning Engineer specializing in MLOps and statistical modeling

Greenwood, SC3y exp

“Integration engineer at ES Foundry who led deployment of ELsentinel, a production EL image-based solar cell quality monitoring system using a Swin Transformer classifier (>0.8 F1 across 15+ classes) plus a live real-time prediction dashboard. Strong in solving messy labeling/data-quality problems with process-team collaboration and shipping ML systems despite limited compute/infrastructure.”

John Chen

Screened

Junior Full-Stack & Data Scientist specializing in ML/NLP and analytics products

Redwood City, CA2y exp

“Built and deployed profitprops.io, a sports betting player-props prediction product using ML/AI. Implemented backend APIs with FastAPI/Express.js and Supabase, trained models on AWS GPU (P3) using Docker + RAPIDS, and set up CI/CD with GitHub Actions while working around cost constraints and data-collection hurdles (EC2 proxy rotation/rate limits).”

Harshavardhan Reddy

Screened

Mid-level AI/ML Data Scientist specializing in NLP, computer vision, and risk analytics

Albany, NY5y exp

“ML/AI engineer with Capital One experience building production-grade customer segmentation and fraud detection systems combining NLP (transformers) and anomaly detection. Strong MLOps and orchestration background (PySpark ETL, MLflow, Airflow, Docker/Kubernetes, Azure ML) with real-time monitoring/alerting and performance optimizations like quantization and caching, plus proven ability to deliver business-facing insights through Power BI/Tableau for marketing stakeholders.”

pavan kalyan padala

Screened

Mid-level Data Scientist specializing in predictive and generative AI

Daytona Beach, Florida4y exp

“AI/ML engineer with production LLM experience in regulated financial services (J.P. Morgan Chase), building a customer response engine to automate first-contact resolution while addressing privacy, bias, compliance, and scale. Strong MLOps/orchestration background (Airflow, Docker/Kubernetes, AWS Step Functions, Azure ML/SageMaker) plus proven ability to integrate with legacy systems and drive stakeholder adoption through dashboards, auditability, and training.”

Akshit Modi

Screened

Mid-level AI/ML Engineer specializing in healthcare NLP and MLOps

Remote, USA5y exp

“Healthcare/clinical ML practitioner who built and productionized ClinicalBERT-based pipelines to extract and standardize oncology EHR data, improving downstream model F1 from 0.81 to 0.92 while controlling training cost via LoRA/QLoRA. Experienced orchestrating real-time AWS ETL/ML workflows (Glue, Lambda, SageMaker) and partnering with clinicians using SHAP-based interpretability, contributing to an 18% reduction in readmissions and full adoption.”

Utkarsh Mittal

Screened

Intern Data Scientist specializing in computer vision and LLM agents

Sunnyvale, CA0y exp

“Software engineering candidate with hands-on experience building and shipping LLM agents: created a production AI enrichment/coding agent at Covalent Metrology using Apollo.io + OpenAI, and built a Mistral hackathon router that dynamically selects among models to reduce token cost while maintaining quality. Also developed a real-time financial margin analysis agent that emails actionable insights and iterated on reliability issues (e.g., fixing misrouted emails, improving news relevance filtering).”

“Analytics-focused candidate with strong SQL, Excel, Python, and Tableau skills who supports payroll-, compensation-, and finance-adjacent processes through rigorous data validation and reconciliation. Stands out for uncovering a duplicate-record mapping issue that exposed roughly $250K in revenue leakage and for building repeatable controls, dashboards, and automated checks to improve reporting accuracy.”

Murali Marupudi

Screened

Entry-level Computer Science graduate specializing in software and engineering

Jersey City, NJ

“Backend engineer focused on high-throughput Python/Flask systems on AWS, with strong scaling and performance tuning experience (e.g., PostgreSQL join reduced from ~3s to <200ms; background aggregation cut from 10 minutes to <90 seconds with 8x throughput). Has also integrated ML model serving into production APIs (churn prediction) using Celery/Redis batching and AWS Lambda/S3, and designed secure multi-tenant architectures with PostgreSQL schema isolation and row-level security.”

“AI/backend engineer who has owned production applied-ML systems end to end, including a Jitsi meeting intelligence platform with custom RoBERTa boundary detection, LLM summarization, and automated retraining from user feedback. Also has healthcare AI experience building a diabetes medication titration system with strict validation, drift monitoring, and safety guardrails—showing both product speed and high-stakes engineering rigor.”

“Computer-vision-focused candidate with internship experience at ASML (Silicon Valley) building object detection models (YOLO, RT-DETR) for SEM defect inspection. Worked end-to-end on preparing multi-resolution datasets and tuning/training strategies, noting improved performance on low-quality images when training jointly on higher-resolution data.”

“Built a production AI-driven QA automation platform using a multi-agent architecture (MCPs + LangGraph) to run parallel website tests across multiple device environments via automated image building and containerization. Currently collaborating with restaurant operators and managers to deliver an agentic restaurant analytics system, emphasizing deep domain discovery with non-technical stakeholders.”

Sandeep Athota

Screened

Mid-level AI/ML Engineer specializing in cloud MLOps and production ML systems

Texas, USA4y exp

“AI/ML engineer at J.P. Morgan Chase who deployed a production financial-risk prediction platform combining CNN/LSTM/gradient boosting on AWS SageMaker, with automated drift-triggered retraining and governance-grade fairness testing. Leveraged SageMaker Clarify plus SMOTE and LLM-generated synthetic data to improve minority-group F1 by 0.12, and communicated results to non-technical risk/ops teams via Power BI dashboards.”

Dinesh Kumar Patibandla

Screened

Mid-level Machine Learning Engineer specializing in LLMs and RAG for finance and healthcare

Texas, USA4y exp

“ML Engineer with recent Goldman Sachs experience building and deploying a production RAG/LLM assistant for summarization, drafting, and internal knowledge retrieval across financial, risk, and compliance documents. Designed for heavy regulatory constraints and scaled to 10,000+ concurrent users using Kubernetes-based orchestration, dynamic LLM routing, and rigorous testing (adversarial prompts, A/B tests, load simulations) with privacy controls like differential privacy.”

HEMANTH KUMAR KOTTAPALLI

Screened

Mid-level Machine Learning Engineer specializing in GPU-accelerated LLMs and MLOps

GA, USA4y exp

“Built and deployed a production LLM-powered decision-support system for supply-chain planners that explains demand forecast changes using grounded retrieval from sales, promotion, inventory, and supplier data. Implemented strict anti-hallucination guardrails and latency optimizations, deployed as a real-time AWS API with monitoring, and reported ~15% forecast accuracy improvement and ~12% supply-chain risk reduction. Experienced orchestrating data/ML/LLM workflows with Airflow, LangChain/LangGraph-style patterns, and AWS Step Functions while partnering closely with non-technical business users via demos and example-based requirements.”

Vamsi Reddy

Screened

Mid-level AI/ML Engineer specializing in healthcare and financial ML systems

Nashville, TN5y exp

“ML/AI engineer with hands-on experience shipping both predictive healthcare models and clinical GenAI assistants into production. They combine strong MLOps depth across Azure and AWS with healthcare-specific safety thinking, including PHI guardrails, retrieval grounding, and production monitoring, and they also built internal Python tooling for fraud ML workflows at Capital One.”

“ML/AI engineer with strong production depth across classical ML, MLOps, LLM/RAG, and scalable Python data platforms, with experience at Cisco and Accenture. Stands out for tying technical decisions to measurable business outcomes, including $1.2M annual savings, 40% faster support resolution, and broad internal adoption of shared engineering frameworks.”

“AI/ML engineer with production experience across both classical ML and Generative AI, including a real-time banking fraud detection platform at Deloitte and a RAG-based cybersecurity threat analysis feature at Accenture. Stands out for owning systems end-to-end—from feature pipelines and model tuning through deployment, monitoring, retraining, and API/platform reliability—with measurable impact on fraud accuracy, false positives, and SOC analyst efficiency.”

“Frontend engineer with JPMorgan experience shipping internal dashboards and real-time trading data interfaces. Stands out for a disciplined AI-assisted development workflow: uses AI selectively for boilerplate, rigorously reviews outputs against actual schemas and architecture, and has hands-on experience with React, GraphQL, WebSockets, Redux, and direct model API integrations.”