.png){kind=logo}
Vetted Deep Learning Professionals
Pre-screened and vetted.
Pavan Sai Reddy Pendry
Junior AI/ML Engineer specializing in GenAI, RAG, and full-stack ML systems
“Built a university campus assistant chatbot (BabyJ/WWJ) using RAG and agentic routing with a FastAPI + React stack and JWT auth, focusing heavily on production concerns like latency and reliability. Uses techniques like speculative prefetching, smart intent routing, and rigorous eval/testing (golden sets, regression, edge cases) while collaborating closely with campus admin/advising teams to iterate based on real user feedback.”
Karthikeya Arra
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and MLOps
“Backend/ML engineering candidate focused on fintech automation who architected a zero-to-one agentic/LLM-enabled system to reconcile messy financial documents and bank transactions, reporting ~40% operational efficiency gains. Experienced migrating monoliths to event-driven microservices with incremental rollout via reverse proxy, and implementing production-grade security (OAuth2/JWT, RBAC, Supabase RLS) plus resilience patterns (timeouts/retries under concurrency).”
Pavithran Gnanasekaran
Junior AI/ML Engineer specializing in RAG, LLM apps, and cloud-native data platforms
“Internship-built full-stack systems spanning HR employee-record portals and internal data-quality dashboards (Flask + SQL + React), emphasizing data integrity and rapid MVP iteration. Also implemented Flask microservices with RabbitMQ for distributed task processing, addressing duplication/ordering issues with idempotency, durable queues, and correlation-ID logging; delivered quantified productivity gains for HR teams.”
Ekta Mulkalwar
Junior Machine Learning & Full-Stack Engineer specializing in applied AI systems
“Master’s thesis focused on building and deploying a gait-based biometric authentication system using mobile accelerometer time-series data as an alternative to passwords/2FA. Emphasized real-world robustness by addressing sensor noise and variability (phone placement, walking speed, footwear) and improving safety using biometric metrics like FAR/FRR and EER, while collaborating closely with a non-ML thesis advisor.”
Lalith Sagar Kambala
Junior Full-Stack Software Engineer specializing in AI-powered SaaS
“Worked on an AI-adjacent search/results product with a React front end and an API-driven backend, focusing on scalability and performance. Emphasizes decoupled JSON API architecture, React rendering optimizations (useMemo/useCallback), and large-dataset techniques like virtualization, plus strong user-issue triage via log analysis and edge-case fixes in query handling/ranking.”
Intern Full-Stack Software Engineer specializing in web apps, IoT systems, and applied AI
Intern Data Scientist specializing in LLM agents, RAG, and real-time ML pipelines
Mid-level AI/ML Engineer specializing in LLM-powered RAG systems and MLOps
Mid-level Data Scientist specializing in GenAI, MLOps, and computer vision for robotics
Junior AI/ML Engineer specializing in LLMs, RAG, and applied NLP
Entry-Level Data Scientist specializing in machine learning, NLP, and cloud analytics
Mid-level Data Scientist specializing in ML, NLP, and Generative AI
Intern Full-Stack & AI Engineer specializing in ML-driven mobile and data platforms
Mid-level AI/ML Research Engineer specializing in NLP, LLM agents, and multimodal systems
Entry-Level Software Engineer specializing in full-stack JavaScript and machine learning
Junior Data Systems Analyst specializing in ML, NLP, and cloud deployment
Junior ML Engineer specializing in search, retrieval, and recommendation systems
Mid-level Software Systems Engineer specializing in cloud infrastructure and AI applications
Intern Full-Stack Software Engineer specializing in web development, data pipelines, and cloud
Entry-level Machine Learning Engineer specializing in LLMs, RAG, and data pipelines