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Vetted Deep Learning Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in LLMs, Generative AI, and MLOps
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Generative AI Engineer specializing in LLM, RAG, and multimodal enterprise solutions
Mid-level AI/ML Engineer specializing in Generative AI and RAG assistants
Mid-level Machine Learning Engineer specializing in healthcare and financial AI
Hemanth Katikala Muniraj
Intern Robotics Software Engineer specializing in ROS2 autonomy and LiDAR localization
“Robotics software engineer focused on production-grade autonomous mobile robot (AMR) navigation in warehouse-style environments, with deep hands-on ROS2/ROS Noetic experience across SLAM, AMCL/NDT LiDAR localization, and Nav2 integration. Strong in real-time debugging and performance tuning using rosbag-driven regression workflows, plus containerized deployment (Docker/Compose) and distributed robot/edge-device communication via MQTT/REST.”
Sahil Gupta
Junior AI Software Engineer specializing in LLM agents, RAG, and healthcare NLP
“Backend engineer who built an agentic LLM system for private equity/finance that answers questions over enterprise contracts and documents using a vector-db RAG pipeline. Differentiator is a trust-focused citation framework (with highlighted source text) to reduce hallucinations in high-stakes workflows, plus strong DevOps experience deploying microservices on Kubernetes with Helm/GitOps and building Kafka real-time pipelines.”
Himanshu Kiran Garud
Mid-level Software Engineer specializing in full-stack systems and applied AI
“Backend/ML engineer who has built both enterprise data pipelines and real-time AI products: modular Python (Flask/FastAPI) services integrating automation scripts and low-latency ML inference (MediaPipe, PyTorch) plus OpenAI-powered feedback. Demonstrated measurable performance wins (~30% faster HR workflows; ~40% faster AWS pipelines across 100+ Oscar Health feeds) and strong multi-tenant/data-isolation patterns (schema-based isolation, RBAC, microservices).”
Tanweer Ashif
Mid-level AI/ML Engineer and Data Scientist specializing in LLMs and MLOps
“Data science/AI intern at University at Buffalo Business Services who built and deployed production systems spanning classic ML and LLM assistants. Delivered real-time competitor intelligence for a Cornell-partnered, $1B beverage launch by scraping/cleaning 5,000+ SKUs and deploying models via API, then built a domain-aware LLM assistant to modernize Excel-based workflows with strong grounding, privacy controls, and sub-5s latency.”
Shashank Chauhan
Mid-level Software Engineer specializing in AI/ML and cloud data platforms
“ML engineer with hands-on experience taking a Gaussian Process Regression-based intelligent survey timing system from build to real-world deployment, including a 3-week RCT on 120 participants and measurable improvements (15% response rate, 23% data quality). Also served as a key technical resource at CData for customer-facing demos and debugging hundreds of production issues, bridging engineering with Sales and Customer Success.”
Nidhi Sharma
Junior Software Engineer specializing in backend systems, AI, and cloud infrastructure
“Built multiple AI-heavy systems with a strong engineering lens on observability, reliability, and real-world usability, including an LLM gateway for auditability/failure isolation and Allyvision, an accessibility tool for visually impaired users. Also owned an end-to-end warehouse shipment tracking dashboard at Addverb Technologies that drove measurable operational gains, combining backend/data depth with frontend product execution.”
Dara McNally
Junior Computer Vision Researcher specializing in deep learning and object detection
“Robotics engineer who built and scaled a distributed perception stack on a Unitree Go1 quadruped, coordinating 5 Jetson Nanos and a Raspberry Pi to capture, aggregate, and stream multi-camera video in real time via UDP/GStreamer and custom ROS nodes. Also implemented a YOLOv9-based detection pipeline enhanced with Grad-CAM-driven selective image enhancement (e.g., MIRNet/UFormer) to improve real-time detections and robot reactions to visual stimuli.”
Pranathi Kamisetty
Intern AI Engineer specializing in LLMs, NLP, and conversational search
“Student building a production trip-planning LLM agent (LangChain + Streamlit) that routes user queries across multiple tools (maps/places/Wikipedia). Implemented zero-shot multi-label intent detection with priority rules to handle multi-intent requests, and collaborates with a startup product manager to shape tone, features, and user experience.”
Pallavi Yellisetty
Mid-level AI/ML Engineer specializing in predictive modeling, NLP, and recommender systems
“AI/ML manager who has deployed production NLP in healthcare—mining unstructured clinical notes and combining them with structured patient data to predict readmissions, with strong emphasis on data alignment and terminology normalization. Also experienced operationalizing ML with Airflow/MLflow and AWS Step Functions/SageMaker, plus stakeholder-facing Power BI dashboards (e.g., marketing customer segmentation).”
Nikhilsai Parimalla
Mid-level Full-Stack Java Developer specializing in Spring Boot microservices and React
“Backend-leaning full-stack engineer who builds and operates Spring Boot microservices with React/TypeScript frontends, using Kafka/RabbitMQ for event-driven workflows. Created an internal ops dashboard for Support/SRE with tracing, alert correlation, and self-serve actions, improving MTTR and reducing escalations while maintaining regulatory-grade reliability and security.”
Faraz Ahmad
Junior Robotics Engineer specializing in autonomous navigation and computer vision for agriculture
“Robotics software engineer who led an autonomous nursery management robot project at Auburn University, spanning RGB-D/IMU sensor fusion, SLAM navigation, and real-time ML for plant detection/quality assessment. Strong ROS1/ROS2 background (C++/Python) with deployment on NVIDIA Jetson, including profiling-driven optimization of YOLO segmentation for real-time behavior and multi-robot (UGV/UAV) communication using ROS2.”
Niharika Govinda
Junior Machine Learning Engineer specializing in NLP, data pipelines, and LLM workflows
“Built and shipped a production LLM-powered decision system that replaced a slow, inconsistent manual review process by turning messy text into structured, auditable outputs behind an API. Demonstrates strong end-to-end ownership of reliability and operations (schema validation, retries/fallbacks, latency/cost controls, monitoring for drift) and a disciplined approach to evaluation and regression testing. Experienced collaborating with non-technical reviewers to define success criteria and deliver interpretable outputs that get adopted.”
Anjali Chandana
Mid-level AI Engineer specializing in NLP, computer vision, and MLOps
“AI Engineer at DXC Technology who has shipped production LLM/NLP systems on AWS (SageMaker, FastAPI) and optimized them for real-time latency and unpredictable traffic using quantization, batching, and autoscaling. Strong MLOps and monitoring discipline (MLflow, CloudWatch, SageMaker Model Monitor) and proven business impact—delivered models with 92% predictive accuracy and cut enterprise decision-making time by 30% through close collaboration with product managers.”
Mahmoud Ayyad
Senior Computer Vision Engineer specializing in AI/ML for scientific imaging
“Computer-vision engineer with hands-on experience designing UAV-based production imaging systems for object detection/tracking, including camera selection and resolution/zoom tradeoffs. Improved segmentation/measurement accuracy by implementing orthorectification using ground points plus intrinsic/extrinsic calibration to correct perspective distortion, and has built Python/OpenCV pipelines (including barcode-focused grayscale processing and multithreaded execution).”
Monthir Ali
Senior AI/ML Engineer specializing in LLMs, RAG, and VR/XR multimodal systems
“PhD researcher (University of Utah) who built a production RAG-powered Virtual Reality Research Assistant to answer lab research questions with concrete citations. Implemented an end-to-end LangChain pipeline using PyPDFLoader, chunking strategies, OpenAI embeddings, and ChromaDB, with emphasis on grounding to reduce hallucinations and ensure research-grade accuracy. Collaborated closely with a non-technical PhD advisor to scope requirements, manage cost constraints, and demo iterative progress.”
Jonathan Grebe
Mid-level Robotics Software Engineer specializing in ROS, C++ and embedded Linux
“Robotics software lead at Icor who grew from intern to owning the end-to-end software lifecycle for a mobile manipulator platform deployed to 300+ customers globally. Deep hands-on ROS2/MoveIt2 and navigation-stack integration (URDF/TF, sensors, behavior engine) plus production infrastructure (CI/CD, OTA, field OS upgrades) and real-world performance tuning for motion planning in EOD multi-robot environments.”
Tejas Belakavadi Kemparaju
Mid-Level Software Engineer specializing in backend, microservices, and ML systems
“Primary designer/implementer/maintainer of an open-source JavaScript library for programmatic SSML generation and validation in text-to-speech pipelines. Focused on safety-by-default APIs with vendor-specific extension adapters, strong backward compatibility/deprecation practices, and measurable performance gains by removing redundant validation stages. Emphasizes developer experience through example-driven documentation and systematic community issue triage.”
Tanisha Pradeep Bhola
Mid-level Software Development Engineer specializing in backend, cloud, and microservices
“Accenture engineer with hands-on experience taking an NLP sentiment analysis system from prototype to production, emphasizing robustness to noisy data, scalability, and observability (dashboards for latency/error/throughput). Also supports customer-facing teams with demos and PoCs, translating client requirements into secure, scalable architectures and troubleshooting LLM/agent workflows via logs and step-level traces.”