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Vetted LSTM Professionals
Pre-screened and vetted.
Angel Contreras
Senior Data Scientist specializing in machine learning, NLP, and MLOps
“ML/NLP engineer with experience building production-grade legal-tech and data platforms, including a GPT-4/LangChain contract review system using ElasticSearch embeddings (RAG) deployed on AWS EKS. Strong in entity resolution and scalable batch/streaming pipelines (Kafka/Spark), with measurable impact (70%+ reduction in contract review time) and a focus on monitoring and CI/CD for reliable delivery.”
Senior AI & Data Engineer specializing in LLM agents, RAG, and data platforms
Director of Engineering specializing in capital markets risk, trading systems, and AI/ML platforms
Mid-level Machine Learning Engineer specializing in real-time fraud detection and edge AI
Staff AI/ML Engineer specializing in NLP, recommender systems, and Generative AI
Executive AI Platform & Innovation Leader specializing in Banking, GenAI, and AI Governance
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and real-time recommendation systems
Mid-level Machine Learning Engineer specializing in reinforcement learning and multimodal AI
Senior Backend Engineer specializing in cloud-scale APIs, data pipelines, and geospatial systems
Senior AI/ML Engineer specializing in Generative AI and LLM applications
Mid-level AI/ML Engineer specializing in GenAI agents and production ML systems
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Mid-level Data Scientist/ML Engineer specializing in LLMs, NLP, and recommender systems
Staff Platform/ML Engineer specializing in agentic AI, RAG, and cloud infrastructure
Mid-level Machine Learning Engineer specializing in fraud detection and recommendations
Mid-level Agentic AI & ML Engineer specializing in LLM agents and RAG systems
Mid-level AI/ML Engineer specializing in cloud MLOps and GenAI for fraud detection
Arnav Singh
Junior Software Engineer specializing in full-stack web, cloud data, and applied ML
“Backend engineer who evolved the X-Ray gaming analytics platform, leading a zero-downtime MongoDB→AWS DocumentDB migration with dual-write, checksum-based validation, and Kubernetes canary rollouts while maintaining real-time monitoring for millions of concurrent sessions. Strong in FastAPI/Python API scaling and performance tuning (cut latency from ~2s to <150ms and reduced DB load 90%) plus production-grade auth/RLS security patterns (JWT, Supabase Auth, PostgreSQL RLS).”
Deekshit Myakala
Mid-level Software Engineer specializing in cloud automation and data/ETL platforms
“Backend engineer with AWS multi-region production experience building APIs and workflow automation for data center/storage hardware operations (firmware orchestration, maintenance checks, ticketing, dashboards). Also shipped an internal AI chat tool that parses hardware runbooks and incorporates user feedback to retrain the model, and has a strong testing/quality discipline (95%+ coverage) plus database performance tuning via indexing and query monitoring.”
Chinmayee Wamorkar
Mid-level Robotics & Autonomy Engineer specializing in MPC, RL, and GPU-accelerated optimization
“Robotics software engineer from Ati Motors who brought a Linear MPC approach (based on Kuhne et al.) into production, rebuilding parts of the planning stack to eliminate oscillations and safely double AMR speed from 0.8 m/s to 1.6 m/s. Also delivered an end-to-end point-cloud detection pipeline (PointPillars) including synthetic data generation in Isaac Sim and TensorRT deployment for real-time human/trolley detection, with a strong focus on production reliability via iterative hardening and nightly SIL.”
Asrith Velireddy
Mid-level AI/ML Engineer specializing in MLOps, LLMs, and scalable ML systems
“ML/LLM engineer at Adobe who deployed a transformer-based personalization and campaign-targeting recommender system end-to-end, including PySpark/Airflow pipelines processing 12M+ events/day and containerized inference on AWS SageMaker (Docker/Kubernetes). Also has hands-on LLM workflow experience (RAG, semantic search, prompt optimization, hallucination mitigation) with a metrics-driven approach to reliability, drift monitoring, and reproducible retraining via MLflow.”