Vetted LSTM Professionals

“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

Screened

Mid-level Software Engineer specializing in cloud automation and data/ETL platforms

Arlington, Virginia6y exp

“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.”

Likhitha Bethi

Screened

Mid-level Software Engineer specializing in backend systems, distributed systems, and applied AI

Stony Brook, NY4y exp

“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.”

“ML/AI engineer with experience at Uber and Scale AI, focused on customer service automation across both classical NLP and generative AI systems. Has owned systems from experimentation through production on AWS, including LLM fine-tuning, RAG optimization, safety evaluation, and internal Python platform tooling that improved consistency and engineering velocity.”

“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

Screened

Mid-level AI/ML Engineer specializing in MLOps, LLMs, and scalable ML systems

Harrison, NJ4y exp

“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.”

“AWS engineer who productionized an internal ML-driven data pipeline from a notebook prototype into a scalable, observable Python service (schema validation, deduplication, idempotency, safe retries, versioned transforms, CloudWatch alarms), reducing manual effort and improving data accuracy/trust. Experienced diagnosing workflow issues in real time (e.g., upstream schema changes) and partnering with account managers/support to unblock adoption of seller-facing Marketplace features by demonstrating reliability with concrete metrics.”

“Built a production internal knowledge assistant using a RAG pipeline over large spreadsheets, PDFs, and support documents, using transformer embeddings stored in FAISS. Focused on real-world production challenges—format normalization, retrieval quality, hallucination reduction (context-only + citations), and latency—using hybrid retrieval, quantization, and containerized deployment, and communicated the workflow to non-technical stakeholders using simple analogies.”

“Robotics software candidate with hands-on ROS2 experience building an autonomous UR7e cake-decorating robot, owning trajectory planning from perception-driven design selection through IK-based waypoint execution. Also optimized a depth-camera object-detection system for assistive glasses (doubling FPS from ~5 to ~10) and is currently exploring distributed Raspberry Pi robot networking to emulate satellite-style handoffs.”

Swarnabha Roy

Screened

Mid-level Robotics & Computer Vision Engineer specializing in autonomous systems and edge AI

College Station, TX6y exp

“Robotics/perception researcher (MVOS Lab, South Dakota State University) who built an end-to-end multimodal RGB-D + LiDAR pipeline for autonomous greenhouse harvesting and 3D plant phenotyping. Demonstrated strong production ownership by diagnosing motion blur with ROS-bag + OpenCV metrics and shipping an edge-deployed, scan-quality-aware workflow that boosted barcode read rate to 98% and supported ~70% autonomous pepper detection/harvesting accuracy.”

Yash Jajoo

Screened

Senior Software Engineer specializing in AI and FinTech platforms

New York City, NY8y exp

“Built a production LLM pipeline at Walter AI that scans massive user inboxes, identifies financial newsletters, and extracts trading strategies into structured JSON for downstream paper-trading workflows. Stands out for combining agent architecture with strong production discipline—cutting scan time from 20 to 5 minutes, reducing LLM costs by 90%, and achieving 3-second P99 latency while handling messy, inconsistent email data at scale.”

“Backend engineer with end-to-end ownership of scientific and AI-powered systems, including neuron imaging pipelines at Monell Chemical Senses Center and an LLM-based structured information extraction platform for Wharton and PSG. Stands out for turning messy, compute-heavy workflows into reliable production backends with measurable impact, including saving researchers over 50 hours per week.”

“Built a production RAG-based "knowledge copilot" for support/ops using LangChain/LangGraph, implementing the full pipeline (ingestion, chunking, embeddings, vector DB retrieval/rerank, guarded generation with citations) and operating it as monitored microservices with CI/CD. Also designed an event-driven, streaming backend for real-time inventory ordering predictions that reduced stockouts by 25%, and has hands-on incident response experience stabilizing LLM API latency/5xx spikes using Datadog/APM and resilience patterns.”