Vetted Predictive Analytics Professionals

“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.”

Ramesh Namperumal

Screened

Executive technology leader specializing in AI/ML, data engineering, and enterprise architecture

Alpharetta, GA27y exp

“Technical founder building an AI IT helpdesk agent startup, currently leading product development as acting CTO/CPO and doing much of the development personally with a small partner group. Brings years of cross-industry technical experience, product/vendor evaluation expertise, and a deliberate strategy to delay outside capital until the product and customer traction are stronger.”

“Backend/DevOps-focused engineer with healthcare and financial systems experience, including an ICU readmission risk platform delivering real-time ML scores via a secure FastAPI service (PyTorch model serving, PostgreSQL, Celery/Redis) deployed on AWS with strong observability. Has hands-on Kubernetes GitOps delivery (Helm, ArgoCD, HPA) and has supported a JPMC on-prem-to-AWS microservices migration using phased validation and blue-green cutovers, plus Kafka/Avro streaming for real-time transaction processing.”

“Full-stack engineer with fintech/trading domain experience (Fidelity) and startup SaaS CRM/billing platform work (Zoho), building real-time portfolio analytics and trade-processing systems. Strong in microservices, event-driven architectures (Kafka/WebSockets), and AWS/Kubernetes operations with measurable performance gains (~34–35% latency reduction) and maintainability improvements (~40% faster deployments). Targeting a founding full-stack engineer role in NYC with meaningful equity.”

Pavan Kumar Malasani

Screened

Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and GenAI

Remote, USA4y exp

“GenAI/ML engineer in Citigroup’s finance environment who has deployed production RAG systems for investment banking under strict privacy and model-risk constraints. Built an internal-VPC Llama2 + Pinecone + LangChain solution with NER redaction and citation-based verification to prevent hallucinations, delivering major time savings, and also partnered with global finance executives to ship an AI early-warning indicator for treasury/liquidity risk.”

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.”

Eduardo Diaz

Screened

Senior Full-Stack Software Engineer specializing in Python, FastAPI/Django, and Azure

Remote13y exp

“Backend/data engineer with production experience building real-time IoT telemetry pipelines for wind/solar assets at Siemens (FastAPI on Azure Event Hubs/Service Bus, Cosmos DB + SQL Server) and deploying GPS/fleet telematics microservices on AWS ECS Fargate with Terraform and blue/green CI/CD. Demonstrated strong reliability and performance chops, including a 30s-to-<100ms SQL optimization and owning a Kafka pipeline incident resolved in ~20 minutes.”

Arpan Acharya

Screened

Executive engineering leader specializing in healthcare IT, cloud platforms, data and AI

Pittsburgh, PA16y exp

“Healthcare technology executive with over a decade in the space who has repeatedly built startup-like businesses inside established companies. As Omnicell's VP of Engineering, Data Analytics & AI, they led platform and cloud transformation efforts, including building the company's first SaaS solution and reshaping business operations beyond engineering. Motivated by high-impact AI and platform opportunities tied to real patient and caregiver pain points.”

“Candidate founded a non-profit and developed unconventional funding mechanisms for it, including a book whose proceeds support the organization and a patented AI stack designed for licensing royalties. They also bring early startup experience and acquisition due-diligence exposure, and are specifically interested in contributing their technical and venture incubation background within an existing entrepreneurial company rather than founding a new one.”

Kishore Koduri

Screened

Director-level Enterprise Architecture leader specializing in AI and platform transformation

Akron, OH29y exp

“Entrepreneurial candidate with deep technology expertise who has already acted as a founder/principal across a real estate venture and early-stage tech startups. Most notably, they raised approximately $1-2M for a real estate project and describe hands-on ownership from ideation and business case development through ROI validation and investor commitment.”

“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.”