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Vetted LSTM Professionals
Pre-screened and vetted.
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
Senior Full-Stack Engineer specializing in cloud-native enterprise and AI platforms
Mid AI/Machine Learning Engineer specializing in LLMs, NLP, and Computer Vision
Mid-level Software Engineer specializing in distributed systems and FinTech platforms
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
Mid-level Data Scientist specializing in FinTech and product analytics
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.”
Suraj Botcha
Intern AI/ML Engineer specializing in LLM systems and industrial AI
“Full-stack AI engineer who has built both document-intelligence products and agentic investigation systems end to end. At ControlRooms.AI, they helped ship a production-facing root cause investigation workflow for industrial operations using Neo4j, FastMCP, RAG, OCR/VLM inputs, and multiple LLMs, contributing to roughly a 10x reduction in manual investigation time. They stand out for designing explainable, traceable AI systems that surface evidence, uncertainty, and missing context rather than forcing overconfident answers.”
Likhitha Bethi
Mid-level Software Engineer specializing in backend systems, distributed systems, and applied AI
“Goldman Sachs engineer who owned end-to-end features for an internal onboarding and case management platform, spanning React/TypeScript UI, a GraphQL gateway, and Node + Spring WebFlux microservices. Built and operated a Kafka-based ingestion and search pipeline with DLQs, retries, idempotency, and strong observability, and improved developer experience via backward-compatible GraphQL API design and schema-driven documentation.”