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Vetted TensorFlow Professionals
Pre-screened and vetted.
Nikitha Kommidi
Mid-level AI/ML Engineer specializing in fraud detection, NLP, and MLOps
“Built a production real-time fraud detection and customer-support automation platform at Citibank, tackling extreme class imbalance (reported ~1:5000) and strict latency constraints. Combines hands-on MLOps (Airflow, Kubernetes, MLflow; Snowflake/Spark/S3 integrations; CI/CD model promotion) with cross-functional delivery to Risk & Compliance focused on interpretability and reducing false positives.”
Kasireddy Kumar reddy
Mid-level AI/ML Engineer specializing in healthcare, fraud detection, and recommender systems
“Healthcare-focused applied ML/LLM engineer who has deployed production systems including an LLM medical documentation assistant that summarizes unstructured EHR notes into physician-ready structured outputs. Experienced building secure, compliant pipelines (PHI minimization, RBAC, encryption) and scaling via Docker/Kubernetes/Azure ML, plus orchestrating ETL/ML workflows with Airflow and Kubeflow; also built an LLM-driven clinical coding assistant at Centene with measurable performance metrics.”
Hsi-Chun Wang
Mid-level Data Scientist specializing in LLM development and scalable ML pipelines
“Built and deployed production LLM pipelines for evidence-based scoring in two domains: biomedical literature mining (scoring ~2700 drug compounds vs gene targets/mechanisms) and long-horizon news analytics (35 years of Chinese articles). Emphasizes reliability at scale (retries/checkpointing/validation), rigorous empirical model benchmarking (GPT-4o/mini/5), and translating results into stakeholder-friendly visual narratives.”
Dylan Zhu
Mid-level Machine Learning Engineer specializing in computer vision and generative AI
“Built and deployed an LLM/RAG system that uses differential privacy and distributional similarity checks to transform private data into a non-sensitive knowledge base while preserving utility. Also has experience demonstrating adversarial ML concepts (FGSM) to non-technical audiences by focusing on observable model behavior rather than implementation details.”
Fnu Pallavi Sharma
Intern Data Scientist specializing in ML, NLP, and MLOps for healthcare and enterprise AI
“Built a production multi-cloud LLM-driven IT ticket automation system using LangGraph, Azure + Pinecone RAG, and an Ollama-hosted LLM on AWS, with Terraform-managed infra and PostgreSQL audit/state tracking for reliability. Also partnered with UW School of Medicine & Public Health students to deliver a glioma survival risk-ranking model, translating clinical feedback into practical pipeline improvements (imputation, site harmonization) and stakeholder-friendly visualizations.”
Ananya Dandi
Junior Machine Learning Researcher specializing in knowledge distillation
“Built and shipped LLM-powered agents including a production RAG research assistant that cut research lookup time from ~20 minutes to ~10–20 seconds using caching, retrieval thresholds, and citation-enforced grounded answers. Also designed multi-step, tool-calling workflows with stateful critique/revision loops and pragmatic monitoring (retry/schema-failure/low-confidence signals) plus normalization/validation layers for messy notes/spreadsheet-style data.”
shivapriya pillalamarri
Mid-level AI/ML Engineer specializing in financial analytics and production ML systems
“Analytics candidate with experience in financial transaction and fraud detection projects, combining SQL data preparation, Python-based automation, and dashboarding. They have owned projects from stakeholder alignment and metric definition through rollout, with emphasis on reducing false positives, improving operational efficiency, and making analytics outputs easy for business teams to adopt.”
Gnaneswar Markat
Mid-level Software Engineer specializing in cloud-native distributed systems
“Full-stack engineer with Bank of America experience building and owning a customer portfolio dashboard end-to-end, from requirements through launch and ongoing iteration. They combine React/Spring Boot/PostgreSQL implementation with strong performance tuning, real-time data handling, and UX improvements, and cite adoption by roughly 12,000 active internal users.”
Pavan Punna
Mid-level AI/ML Engineer specializing in LLMs, MLOps, and healthcare-fintech AI
“Built and owned a production GPT-4 RAG assistant for clinical and enterprise query resolution, taking it from initial experiment to deployment, monitoring, and iterative improvement. Their work cut resolution time from 45 minutes to under 2 minutes, achieved roughly 95% accuracy, and scaled to thousands of additional monthly queries while emphasizing safety and trust in a sensitive clinical domain.”
Duncan Freeman
Staff Machine Learning Engineer specializing in NLP, LLMs, and document intelligence
“ML/AI engineer at PNC who has shipped enterprise-grade RAG and document intelligence systems for compliance and policy workflows. Stands out for combining LLM product thinking with production rigor—owning FastAPI/Kubernetes deployments, monitoring, evaluation, and human-feedback loops that drove measurable gains like 40% faster policy search and 30% faster compliance review.”
premkumar narla
Mid-level AI/ML Engineer specializing in GenAI, RAG, and enterprise ML systems
“ML/AI engineer with hands-on experience at Morgan Stanley building production fraud detection and enterprise RAG systems. Stands out for owning systems end-to-end—from experimentation and deployment to monitoring and iteration—and for delivering measurable impact, including an 18% reduction in fraud false positives, 40% lower inference latency, and internal tooling that reduced model deployment time from days to hours.”
ASHISH DONDAPATI
Mid-level AI/ML Engineer specializing in Generative AI for Financial Services
“ML/AI engineer with strong financial-services domain experience who has built production systems spanning trade anomaly detection, investment-research RAG, and agentic LLM workflows. Particularly compelling for teams needing someone who can take ML/GenAI from prototype to monitored production while balancing compliance, latency, cost, and reliability.”
Rahul Manne
Mid-level Software Engineer specializing in .NET, Azure, and enterprise platforms
“JavaScript/React/TypeScript engineer with hands-on open-source experience improving a hooks utility library—fixed a reported async race condition that reduced unexpected re-renders and added a debounced callback hook that became widely used. Brings a production-minded approach to performance and abstractions (APM/metrics-driven, DB/caching focus) with strong testing, documentation, and community support practices.”
Sujeeth Rajkumar
Mid-level Generative AI Engineer specializing in LLMs and enterprise AI
“Built and owned an enterprise LLM/RAG document intelligence platform for PNC Financial Services in a compliance-heavy environment, focused on grounded answers over internal finance and policy documents. Stands out for combining GenAI product delivery with production engineering discipline, delivering 60% faster document review and materially better answer quality while creating reusable FastAPI-based AI services for multiple teams.”
Rushabh Thakkar
Mid-level Machine Learning Engineer specializing in NLP, computer vision, and LLMs
“Wayfair ML/AI engineer who has shipped and operated production LLM systems for both internal analytics and customer-facing assistants. Stands out for combining strong RAG/retrieval engineering with production-grade platform work—improving trust, reducing latency by ~30%, and cutting ad hoc reporting demand by ~50%.”
Naiya Adatiya
Mid-level Software Engineer specializing in backend systems and cloud-native microservices
“Engineer with a process-driven approach to AI-assisted software development, focused on orchestrating where AI adds value while maintaining human review and verification. Has applied this in backend work such as an S3-based invoice pipeline and used multi-agent workflows to speed up large API refactors across many endpoints.”
Sanjana Chowdary
Intern Full-Stack Engineer specializing in web applications and AI
“Engineer with hands-on experience both using AI coding agents in production and building AI systems, including chatbot development and BERT fine-tuning at Atos. At Groupr, they applied strong systems judgment to live operational workflows, validating concurrency decisions manually for an admin portal supporting 500+ orders per day.”
Hrishikesh Gawde
Mid-level Software Engineer specializing in AI and FinTech platforms
“LLM/agentic systems practitioner who specializes in moving demo-only assistants into reliable, observable, cost-controlled production services. Strong in real-time diagnosis of complex agent workflows (including tracing, loop detection, and guardrails) and in customer-facing enablement—running workshops, building tailored PoCs, and partnering with sales to close deals by proving reliability in high-risk pilots.”
vedavathi thumula
Mid-level AI/ML Engineer specializing in Generative AI and agentic systems
“Backend/platform engineer who has owned a Python/FastAPI results API and deployed it on Kubernetes with Helm and GitHub Actions-driven CI/CD. Demonstrates strong production operations mindset across performance tuning, monitoring, safe rollouts/rollbacks, and phased migrations, plus hands-on Kafka streaming experience focused on ordering and idempotency.”
Anudeep Eloori
Mid-level Software Developer specializing in full-stack enterprise applications
“Software engineer with experience building and iterating high-volume Spring Boot microservices on AWS (Docker/Kubernetes) and integrating with React front-ends. Also delivered an LLM-powered document summarization system using embeddings + retrieval (RAG) with grounding/guardrails and built evaluation loops that directly drove retrieval and chunking improvements. Has scaled Kafka-based pipelines processing millions of messy financial/infrastructure records with reliability and cost/latency tradeoff management.”
Aditya Rao
Mid-level Software Engineer specializing in backend, AI, and distributed systems
“Software engineer with 4.5 years of startup experience across programmatic advertising, health tech e-commerce, and automobile diagnostics, plus both bachelor's and master's degrees in CSE. Built an agentic global supply chain platform in a hackathon using a highly structured AI-first workflow, and has hands-on experience designing multi-agent debate systems, rollout safeguards, and observability-driven production fixes.”
Shirisha K
Mid Software Engineer specializing in backend microservices and FinTech systems
“Full-stack engineer with experience shipping analytics dashboards and an AI-driven support assistant for a cloud analytics platform. They combine Java/Spring Boot backend work with TypeScript frontend development and showed practical knowledge of LLM production concerns like retrieval grounding, latency, caching, retries, and graceful fallbacks. Their shipped dashboard feature improved load times by 35-40% and reduced support issues tied to delayed analytics.”
Rudra Dudhat
Entry-level Applied AI Engineer specializing in LLMs and ML systems
“AI automations intern at a lean US-based marketing agency who works directly with founders and builds practical GTM systems end-to-end. He combines ML/LLM tooling with outbound execution, including a clustering-based recommender that improved client lead generation by 30% in two weeks and a personal cold outreach engine that achieved a 12%+ reply rate.”
Krishna Sristi
Entry Data Scientist specializing in ML, NLP, and GenAI
“AI/full-stack engineer who has built a production-style LLM knowledge assistant from scratch, combining FastAPI, LangChain, FAISS, semantic retrieval, and a user-facing chat interface. Stands out for owning both the technical architecture and the product usability layer, including latency optimization, prompt refinement, and source-backed responses to improve trust for non-technical users.”