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Vetted LSTM Professionals
Pre-screened and vetted.
Mid-level AI Data Scientist specializing in financial risk, fraud detection, and NLP/LLM systems
Senior Data Scientist specializing in Generative AI, NLP, and MLOps
Abhishikth Meesala
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and fraud detection
“At PwC, built and productionized an agentic RAG enterprise search assistant over 6M internal documents (8M embeddings), deployed across AWS and GCP. Drove major retrieval gains (72%→92% precision via BM25+dense hybrid with RRF and cross-encoder re-ranking), reduced hallucinations 30%, achieved <2s latency at 50–60K queries/month, and cut support tickets 30%—boosting adoption to 2,500 users by adding source-cited answers.”
“Built and deployed a production RAG-based LLM Q&A and summarization platform for internal documents, emphasizing grounded answers with structured prompting and citations to reduce hallucinations. Experienced orchestrating end-to-end LLM workflows with LangChain plus cloud pipelines (Azure ML Pipelines, AWS), and runs iterative evaluation using both metrics (accuracy/hallucination/latency/cost) and real user feedback to drive reliability.”
Priyanshu Maurya
Mid-level Data Scientist specializing in insurance, finance, and healthcare analytics
“Built and productionized LLM-driven sentiment scoring for earnings call transcripts at Goldman Sachs, replacing legacy NLP to deliver a cleaner trading signal while managing latency/cost via batching, caching, and distilled models. Also implemented an Airflow-orchestrated fraud modeling pipeline at MetLife with drift-based retraining and SageMaker deployment, and has a disciplined evaluation/rollout framework for reliable AI workflows.”
David Kidwell
Senior AI/ML Data Scientist specializing in NLP, computer vision, and MLOps
“Applied LLMs and a graph-RAG architecture in Neo4j to automate an accounting firm's cross-checking of transactional books against tax regulations, indexing 1,000+ pages into a knowledge graph with vector search. Combines agentic LLM workflows with classical NER (Hugging Face/NLTK) and validates using expert-labeled held-out data plus precision/recall and measured accountant time savings after deployment.”
Staff Machine Learning Engineer specializing in LLM agents and ML systems
Intern Software Engineer specializing in cloud infrastructure and distributed systems
Mid-level AI/ML Engineer specializing in Generative AI and RAG systems
Intern AI/Data Science Engineer specializing in LLM agents, data engineering, and predictive analytics
Mid-level Machine Learning Engineer specializing in NLP, recommender systems, and MLOps
Junior Software Engineer specializing in LLMs, RAG, and Knowledge Graphs
Mid-level AI/ML Engineer specializing in NLP, MLOps, and financial risk & fraud analytics
Senior AI/ML Engineer specializing in GenAI, MLOps, and healthcare analytics
Senior AI/ML Engineer specializing in Generative AI and Computer Vision
Mid-level AI/ML Product & Solutions Specialist specializing in GenAI and MLOps
Principal Data Scientist specializing in AI/ML forecasting and MLOps
Mid-level AI/ML Engineer specializing in forecasting, MLOps, and generative AI
Principal Data Scientist specializing in LLMs, RAG, and enterprise AI products
Ibrahim Kurban Ozaslan
Junior Robotics & Controls Researcher specializing in optimization, MPC, and reinforcement learning
“Robotics software candidate who designed and implemented a hierarchical motion-planning and whole-body control pipeline for a 37-state Spot robot to traverse complex terrain, using Graph of Convex Sets for safe footstep selection plus optimization-based IK and nonlinear trajectory optimization for joint trajectories/contact forces. Strong in optimization-heavy robotics workflows (PyDrake, MATLAB/Simulink) and methodical debugging down to signal-level and numerical stability; has not used ROS/ROS2 yet.”
Swathi Sankaran
Senior Python Full-Stack Developer specializing in cloud, data engineering, and ML/GenAI
“Backend/data engineer with hands-on production experience building FastAPI services on AWS and implementing strong reliability/observability (CloudWatch, ELK, correlation IDs, alarms). Has delivered serverless + container solutions with IaC (CloudFormation/Terraform) and Jenkins CI/CD, and built AWS Glue/PySpark pipelines into S3/Redshift with schema-evolution and data-quality safeguards; demonstrated large-scale SQL tuning (45 min to 3 min on a 500M-row workload).”
Anvith Reddy Dodda
Mid-level AI Engineer specializing in GenAI, NLP, and MLOps
“LLM/agentic-systems engineer with PayPal experience hardening an LLM-powered fraud support assistant from prototype to production, focusing on low-latency distributed architecture, rigorous evaluation/testing, and security/compliance. Comfortable in customer-facing and GTM contexts—runs technical demos/workshops, builds tailored pilots, and aligns sales/CS with engineering to close deals and drive adoption.”