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Vetted Matplotlib Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in NLP, federated learning, and fraud detection
Intern AI Researcher specializing in NLP, multimodal generative AI, and medical imaging
Mid-level Data Scientist specializing in GenAI, NLP, and deep learning
Mid-Level Software Development Engineer specializing in AWS serverless, security, and ML platforms
Mid-level Machine Learning Engineer specializing in recommender systems and LLM/RAG pipelines
Mid-level AI/ML Engineer specializing in GenAI, LLMs, and RAG pipelines
Senior Data Scientist specializing in Generative AI/NLP for legal and healthcare domains
Intern Machine Learning Engineer specializing in RL post-training for LLMs and VLMs
Mid-level AI/ML Data Engineer specializing in data pipelines, MLOps, and LLM/RAG systems
Senior Data Analytics & Applied ML Engineer specializing in LLMs, RAG, and MLOps
Mid-level Full-Stack Developer specializing in cloud-native apps, AI/ML, and microservices
Mid-level Robotics & Firmware Engineer specializing in continuum robotics and SerDes validation
Junior AI/ML Engineer specializing in agentic AI and cloud optimization
Gagan Mundada
Intern Machine Learning Engineer specializing in multimodal AI and evaluation benchmarks
“ML-focused candidate with beginner ROS/ROS2 experience (custom pub-sub nodes; TurtleBot3 SLAM simulation debugging via topic inspection and transform/orientation checks). Has research/project exposure to LLM training approaches (GRPO with pseudo-labels using Hugging Face TRL on Qwen/Llama) and uses Docker/Kubernetes + CI/CD to run ViT saliency-attention/compression workloads on UCSD Nautilus infrastructure.”
Sharath Nyalakonda
Mid-level AI/ML Engineer specializing in NLP, graph models, and MLOps for FinTech and Healthcare
“AI/ML engineer who has deployed production LLM/transformer-based systems for merchant intelligence and fraud/support optimization, delivering +27% merchant engagement and +18% payment success. Deep experience in privacy-preserving, PCI DSS-compliant data/ML pipelines (Airflow, AWS Glue, Spark, Delta Lake) and scalable microservices on Kubernetes, plus proven cross-functional delivery in healthcare claims analytics at UnitedHealth Group (12% HEDIS claim reduction).”
Vidyaa KrishnanNivash
Mid-level Robotics Software Engineer specializing in perception and motion planning
“Robotics software engineer focused on ROS2 motion and calibration systems—built a trajectory generator/low-level controller using TOPPRA that improved robot motion speed by 11x while increasing accuracy. Experienced making high-frequency robot communication more real-time (core isolation) and shipping ROS2 modules via Docker-backed CI/CD, including serving as release manager coordinating reviews, release notes, and QA.”
Po Jui Lin
Mid-Level Full-Stack Engineer specializing in cloud platforms, cybersecurity web apps, and IoT
“Backend engineer with experience at Amazon building an API-driven service (APS) for large-scale prompt optimization jobs using AWS Step Functions, Batch/Fargate, DynamoDB, and S3, emphasizing idempotency, observability, and secure execution boundaries. Also led a multi-tenant enterprise policy/configuration backend refactor at MAMIT Cyber with versioned schemas, shadow writes, feature-flagged rollout, and PostgreSQL RLS-based tenant isolation.”
Sri Lekha Kandadai
Mid-level Machine Learning Engineer specializing in MLOps and multimodal AI
“ML/AI engineer focused on production-grade model reliability: built a monitoring and validation framework to detect drift, trigger anomaly alerts/retraining, and maintain consistent performance for device intelligence workflows at scale. Strong MLOps background with Python pipelines, Docker/Kubernetes deployments, Airflow orchestration, and real-time monitoring dashboards; experienced partnering with product managers to deliver business-facing insights.”
Jayanth Godi
Mid-level Site Reliability Engineer specializing in cloud infrastructure, Kubernetes, and LLM applications
“SRE-focused engineer with experience at Sony Interactive Entertainment productionizing high-throughput LLM/agentic systems on Kubernetes, including GPU-aware autoscaling and warm-pool strategies to manage latency and cost under traffic spikes. Demonstrates strong incident response using Prometheus/Grafana + Jaeger tracing (e.g., resolving recursive agent loops and restoring 99.9% availability within minutes) and partners closely with sales/customer teams through PoV demos and developer workshops.”