Vetted LightGBM Professionals

“ML/AI engineer with production experience across retail and healthcare: built a real-time computer-vision shelf monitoring system at Walmart and optimized edge inference latency by ~30% using TensorRT/ONNX and pruning. Also partnered with CVS Health clinical/pharmacy teams to deliver a medication-adherence predictive model, using Streamlit explainability dashboards and achieving an 18% adherence improvement.”

“Unreal Engine UI engineer focused on scalable, production-ready UI architecture (C++/Slate/UMG/CommonUI) with strong designer enablement via decoupled, interface-driven patterns and MVVM. Demonstrated measurable performance wins: replaced 200+ per-frame Blueprint bindings to cut UI prepass/paint from 4.2ms to 0.5ms and reduced VRAM by ~120MB using texture streaming proxies.”

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.”

“ML/AI practitioner with hands-on experience shipping production time-series forecasting and RAG-based customer insights platforms in an enterprise setting. At BASF, he improved seed sales forecasting beyond naive baselines using model selection tailored by brand size, and he also led a RAG solution over Salesforce reports, complaints, and surveys that reached 2,000+ users with strong daily engagement.”

“ML/AI engineer with hands-on ownership of a production LLM/RAG system at Goldman Sachs, focused on workflow automation and large-scale document search for operational teams. They combine strong MLOps and backend engineering skills with practical GenAI evaluation and safety practices, and cite measurable impact including 22% better task guidance accuracy and sub-second search across millions of records.”

“Completed an Amazon externship building a GPT-4 + RAG pipeline to summarize themes from hundreds of employee reviews for workforce analytics aimed at improving warehouse retention. Emphasizes production-readiness through labeled-data evaluation, source attribution for explainability, human-in-the-loop review, and rigorous data cleaning/observability to debug real-world LLM workflow issues.”

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.”

Atyab Hakeem

Screened

Junior Data Scientist / ML Engineer specializing in GenAI and computer vision

San Francisco, CA2y exp

“Software engineer who built and deployed OddPulse, a multi-agent LLM-powered continuous financial auditing system aimed at reducing compliance penalties by catching issues before audit cycles. Experienced with TrueAI-based agent orchestration, Airflow on GCP batch workflows, and rigorous evaluation/benchmarking (hit rate/MRR, latency/TTFT, cost) alongside security controls for sensitive financial data.”

Pandari G

Screened

Mid-level Machine Learning Engineer specializing in Generative AI and RAG systems

San Francisco, USA5y exp

“GenAI/LLM engineer with production deployments in both fintech and retail: built an AI-powered mortgage document analysis/automated underwriting pipeline at Fannie Mae (OCR + custom LLM) cutting underwriting review from 3–4 hours to under an hour with privacy-by-design controls. Also helped build Sephora’s GenAI product advisory bot using LangChain-orchestrated RAG (Azure GPT-4, Azure AI Search, MySQL HeatWave vector search), focusing on grounding, evaluation, and compliance-aware architecture choices.”

Sathvik Maridasana Nagaraj

Screened

Mid-level AI/ML & GenAI Engineer specializing in LLMs, RAG, and MLOps

5y exp

“LLM/agent engineer with production experience in healthcare claims automation, delivering large operational impact (cut case handling from ~8–10 minutes to ~3 minutes, ~2,000 staff hours saved/month at ~3,000 claims/month). Built resilient Azure-based deployments (Azure DevOps CI/CD, Docker/FastAPI, Redis caching, autoscaling, observability) and improved reliability via safety/evaluation frameworks that reduced hallucinations by 32%.”

“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.”