Vetted OpenCV Professionals

Niyaz Nurbhasha

Screened

Mid-level Machine Learning Engineer specializing in computer vision and LLM pipelines

4y exp

“ML/LLM engineer who built production systems to speed up artist content-creation workflows, including a fine-tuned image captioning model paired with a RAG layer over image embeddings/captions to improve consistency across changing domains. Experienced orchestrating multi-tool agents with LangChain/LangGraph (planning + critic/reflection) and setting up practical monitoring (caption rejection rate) plus evaluation sets for tool-calling accuracy, output quality, and latency.”

Shriya Bannikop

Screened

Mid-level Software Engineer specializing in cloud platforms, data engineering, and distributed systems

Seattle, WA5y exp

“Full-stack engineer who built and owned an AI-assisted job-matching dashboard in Next.js App Router/TypeScript, keeping LLM logic server-side and improving performance via deduplication, caching/revalidation, and streaming (35% fewer duplicate LLM calls; 40% faster first render). Also has strong data/backend chops: designed Postgres models and optimized queries at million-record scale (1.8s to 120ms) and built durable AWS multi-region telemetry workflows with idempotency, retries, and monitoring.”

Parth Gupta

Screened

Junior Robotics & Computer Vision Engineer specializing in perception and autonomy

Ames, IA2y exp

“Robotics engineer with capstone experience building an autonomous food-assembly robot arm, owning perception/deep learning (SAM2-based segmentation) and a model-based RL manipulation policy for deformable food items while also serving as project manager. As a robotics engineering intern at Salin247, optimized an autonomous farm vehicle perception stack to hit 20 FPS by cutting latency from 200ms+ to ~40ms using GPU acceleration (CUDA OpenCV, CuPy) and multiprocessing, and built ROS 2 nodes for real-time perception and streaming.”

“GenAI/LLM engineer who architected and deployed a production RAG “research assistant” for JPMorgan Chase’s regulatory compliance team, focused on safety-critical behavior (mandatory citations, refusal when evidence is missing). Deep hands-on experience with LlamaIndex, Pinecone, Hugging Face embeddings, LangGraph agent workflows, and metric-driven evaluation (golden sets, TruLens), including a reported 28% relevancy lift via cross-encoder re-ranking.”

“AI-focused engineer who treats models as tightly controlled collaborators rather than autonomous replacements. Built and led a LangGraph-based multi-agent research system with separate stages for decomposition, retrieval, synthesis, and validation, emphasizing modularity, debuggability, and robust failure handling.”

“Robotics software intern (Summer 2025) at Ola Krutrim working on 2W/4W ADAS: integrated an ASM330LHH IMU over I2C, performed camera-LiDAR intrinsic/extrinsic calibration, built an interactive calibration GUI, and optimized a camera-LiDAR fusion pipeline (cut latency from ~500ms to ~200ms) including CUDA parallelization and Kalman filter-based lane tracking. Strong ROS 2 background with URDF/Gazebo simulation and custom ROS2 Arduino bridge work for hardware control.”

“Backend/data engineer with hands-on production experience across FastAPI/PostgreSQL APIs and AWS (Lambda, ECS) delivered via Terraform + GitHub Actions. Built Glue-based ETL pipelines into Redshift with schema evolution and data quality checks, modernized legacy reporting into Python microservices, and has demonstrated measurable SQL performance wins (multi-second query reduced to sub-300ms).”

“Robotics software engineer/researcher at Stanford PDML Lab building VisualFT, a ROS2-based visual-tactile sensing system for compliant force-control guidance in acupressure/ultrasound-style manipulation. Also interned at Neocis (dental robotics) improving safety-critical collision detection using Bullet Physics with automated validation and CI (Jenkins/CDash).”

“Summer SDE intern at AWS who built and deployed a column-lineage debugging tool for on-call engineers, using AWS Bedrock to parse SQL and generate a column DAG. Integrated the tool into an existing validation system and hardened it against real-world SQL format differences via flexible parsing and testing with queries from multiple upstream teams.”

“Backend-focused full-stack engineer who built an MVP at Neon AI for PhD students: a FastAPI backend integrating multiple cloud and local LLMs plus a RAG pipeline with session/identity management, designed to be modular and extensible across domains. Also has VMware experience debugging production issues and executing safe, API-compatible refactors with staged rollouts and strong security controls.”

Swagat Adhikary

Screened

Junior Software Engineer specializing in LLM agents and FinTech platforms

Raleigh, NC1y exp

“AI/LLM engineer with Fidelity Investments experience who built and shipped a production GraphRAG system that augmented prompts with codebase context, improving business analyst efficiency by 15% and saving ~$3.5M annually. Strong in AWS EKS/Kubernetes/Helm and enterprise IAM/OIDC patterns (including cross-account S3 access), with experience mentoring interns and collaborating with non-technical leaders to extend AI pipelines (e.g., adding SQL functionality during MVP).”

Sakshi Dinesh Deore

Screened

Mid-level Software Engineer specializing in AWS, DevOps automation, and data platforms

Bellevue, USA3y exp

“Engineer with Securonix experience deploying and operating production microservices and real-time data-processing systems at high throughput. Led AWS infrastructure, CI/CD, monitoring, and customer-driven customization for a threat-report classification solution, including rule adjustments and model retraining based on live client feedback.”

“ML/AI engineer with strong end-to-end production ownership across predictive ML and Generative AI use cases. They built a churn prediction platform that cut churn 12% and preserved about $1.2M in annual revenue, and also shipped a RAG-based support assistant that reduced ticket resolution time 30% while improving agent satisfaction and onboarding speed.”

Abhirup Chakraborty

Screened

Junior Software Engineer specializing in backend systems, AI, and search

Champaign, IL3y exp

“Built a complex graph-based search engine to find connections between people and has hands-on experience designing multi-agent coding pipelines that move features through implementation, test generation, testing, and sanity checks. Stands out for treating AI agents like an engineering team, with shared-memory coordination, queue signaling, and completeness-focused guardrails to improve reliability and reduce ambiguity.”

“ML/AI engineer with hands-on ownership of both classical ML and GenAI systems in production. They built an end-to-end churn prediction service on AWS and also shipped RAG-based document search/summarization features, with clear experience in monitoring, hallucination reduction, cost/latency optimization, and creating shared Python/LLM infrastructure used across teams.”