Vetted LSTM Professionals

“Data professional with ~4 years of experience, most recently at AIG (insurance), building ML/NLP systems for fraud detection and policy automation using transformers, CNNs, and clustering/anomaly detection. Also developed a RAG-based knowledge retrieval system, iterating across embedding models and moving to production based on precision and latency SLAs, then containerizing and deploying with SageMaker and CI/CD.”

PRAHARSHA JANDHYALA

Screened

Mid-level Data Scientist/Data Analyst specializing in ML, BI dashboards, and ETL pipelines

Dallas, TX4y exp

“Data/ML practitioner with experience at Humana and Hexaware, focused on turning messy, semi-structured datasets into production-ready pipelines. Built an age-prediction model from book ratings using heavy feature engineering and multiple regression models, and has hands-on entity resolution (deterministic + fuzzy matching) plus embeddings/vector DB approaches for linking and search relevance.”

Prashanth Kedri

Screened

Mid-level Machine Learning Engineer specializing in MLOps, NLP, and predictive maintenance

AL, USA4y exp

“ML engineer with General Motors experience deploying production AI systems, including a BERT-based sentiment classifier for over a million customer support call transcripts (reported ~91% precision) and sub-200ms latency via FastAPI/Docker optimization. Also built predictive maintenance models and automated retraining/monitoring workflows using Airflow and MLflow, collaborating closely with non-technical customer support stakeholders.”

Sabita Kumari

Screened

Senior Full-Stack AI Engineer specializing in LLM/RAG agentic systems

Boston, MA11y exp

“Built and deployed JobMatcher AI, an LLM-driven workflow automation product for job seekers that extracts requirements from job descriptions, matches to user skills, and generates tailored outreach. Demonstrated strong production engineering by cutting per-run cost ~70%, improving reliability with retries/backoff/fallbacks, and reducing hallucinations via schema validation and templating; also orchestrated the system with LangGraph plus Docker Compose across API, vector DB, and workers.”

Sachin Dulla

Screened

Mid-level AI/ML Engineer specializing in NLP, fraud detection, and MLOps

Kentwood, MI3y exp

“Built and deployed a domain-specific LLM chatbot for research/support, cutting manual effort by ~50%. Demonstrates strong applied LLM engineering: RAG, prompt grounding with citations and fallbacks, embedding/top-k tuning, and production monitoring (confidence, latency, feedback loops). Experienced orchestrating agent workflows with LangChain-style pipelines and continuous evaluation to maintain reliability.”

Bhavishyasai Chigurupati

Screened

Mid-Level Data/ML Engineer specializing in Generative AI and cloud data platforms

Overland Park, KS5y exp

“Built and productionized an LLM-based financial document analysis system using a RAG pipeline, including robust ingestion/chunking/embedding workflows, vector DB retrieval, and an AWS-deployed FastAPI service containerized with Docker. Demonstrates strong applied expertise in improving retrieval quality and latency at scale, plus hands-on experience debugging agentic/LLM workflows with monitoring and trace-based analysis while supporting demos and customer-facing adoption.”

“AI/LLM engineer who has shipped production RAG copilots and multi-agent workflows, including a real-time Llama3 (Ollama) copilot backend handling 12k+ concurrent queries at 99.9% uptime. Deep on orchestration (Langflow/Airflow/Kubernetes), reliability evaluation (hallucination detection, p95 latency, token cost), and monitoring (Prometheus/Grafana), with demonstrated stakeholder-facing analytics delivery via Tableau.”

“Entry-level candidate who built NYC Lens, a real-time Gemini-based multi-agent system that processes live camera input, identifies landmarks, and returns structured contextual insights. Despite being a fresher, they show hands-on experience with deployment on Cloud Run, modular orchestration, noisy-data handling, and reliability patterns like retries, fallbacks, and explicit state management.”

SaiSindhu Beeravolu

Screened

Mid AI/ML Engineer specializing in LLMs, RAG, and healthcare AI

Illinois, USA4y exp

“Healthcare ML/AI engineer with production experience at UnitedHealth Group, including an end-to-end readmission prediction system built on 50M+ patient records that improved accuracy by 18% and reduced preventable readmissions by 12%. Also shipped a clinically grounded LLM/RAG referral generator with human-in-the-loop safety controls, showing strong depth in regulated, high-stakes AI systems.”

Almas Fathimah

Screened

Senior AI Engineer specializing in Generative AI and ML platforms

USA7y exp

“Built and owned a production RAG-based conversational AI system at Entera for real estate analysis, taking it from experimentation through AWS deployment, monitoring, and iterative improvement. Demonstrates strong practical judgment in retrieval design, LLM safety, and scalable Python service architecture, with measurable impact including 30-40% reduction in manual analysis time and roughly 30% better response accuracy.”

“Frontend engineer who built a production GPS trip tracking system at MTM Transit for fraud investigators and operations teams, turning raw vehicle telemetry into an interactive map and timestamp workflow. Stands out for hands-on experience with React/TypeScript, Google Maps, and performance tuning of heavy geospatial UIs handling thousands of pings.”

Saketh Kota

Screened

Mid-level Data Scientist / ML Engineer specializing in Generative AI, RAG, and MLOps

Irving, TX4y exp

“Built and productionized a RAG-based LLM research assistant for biomedical and regulatory document search using Mixtral 7B on SageMaker, LangChain, and Milvus, cutting research time by ~40%. Has hands-on multi-cloud MLOps experience across AWS/Azure/GCP with Kubeflow/Airflow/Composer plus Terraform + ArgoCD, and applies rigorous evaluation/monitoring (latency, accuracy, hallucinations). Also partnered with a non-technical PM to deliver an insurance policy Q&A chatbot that reduced customer response time by 30%+.”