Vetted Transformers Professionals

“Full-stack engineer with fintech trading-platform experience who shipped and operated a real-time portfolio P&L/performance feature end-to-end (React + Node/WebSockets + MongoDB) on AWS, including significant performance tuning under peak trading load. Also built a Spark-based trading analytics pipeline with idempotency and reconciliation for auditability, and has a personal React/TS + Node/Express project (Artsy) with JWT auth and schema-evolution practices.”

Kasireddy Kumar reddy

Screened

Mid-level AI/ML Engineer specializing in healthcare, fraud detection, and recommender systems

Missouri, USA6y exp

“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.”

Pavan Punna

Screened

Mid-level AI/ML Engineer specializing in LLMs, MLOps, and healthcare-fintech AI

Dallas, TX5y exp

“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.”

ASHISH DONDAPATI

Screened

Mid-level AI/ML Engineer specializing in Generative AI for Financial Services

San Francisco, USA6y exp

“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.”

“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.”

“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.”

“LLM/RAG engineer with production deployments in highly regulated domains (Frost Bank and GE Healthcare). Built secure, explainable document-grounded Q&A systems using LoRA fine-tuning, strict RAG with confidence thresholds, and citation-based responses; also established evaluation/monitoring (golden QA sets, hallucination tracking, drift) and achieved ~40% latency reduction through retrieval/prompt tuning.”

“Built a production AI-driven contract/document extraction system combining OCR, normalization, and LLM schema-guided extraction, orchestrated with PySpark and Azure Data Factory and loaded into PostgreSQL for analytics. Emphasizes reliability at scale—using strict JSON schemas, confidence scoring, targeted retries, and multi-layer validation to control hallucinations while processing thousands of PDFs per hour—and partners closely with non-technical business teams to refine fields and deliver usable dashboards.”

“Full-stack engineer focused on enterprise, cloud-native microservices—building Spring Boot backends and React/Angular front ends with strong security (OAuth/JWT), AWS infrastructure (RDS/S3), and containerized deployments (Docker/Kubernetes). Has delivered data-heavy order/account/transaction platforms and healthcare solutions including EHR integrations for secure patient data exchange, with emphasis on testing, performance tuning, and reliability (load testing).”

“AI/ML engineer with healthcare domain depth who led a HIPAA-compliant, production LLM system at McKesson to automate clinical document understanding—extracting entities, summarizing provider notes, and supporting authorization decisions. Hands-on across Spark/Python ETL, Hugging Face + LoRA/QLoRA fine-tuning, RAG, and cloud-native MLOps (Airflow/Kubernetes/Step Functions, MLflow, blue-green on EKS/GKE), with explicit work on PHI handling and hallucination reduction.”

“Built and deployed a GPT-based RAG enterprise search system for healthcare clinicians, emphasizing low-latency performance and reduced hallucinations while maintaining end-to-end HIPAA compliance. Demonstrates deep applied experience with PHI-safe data governance (detection/redaction/de-identification), secure Azure ML deployment patterns, and orchestration of production LLM workflows using LangChain and Airflow.”

“ML/LLM engineer who has shipped production RAG systems (LangChain + HF Transformers + FAISS) with hybrid retrieval and cross-encoder re-ranking, deployed via FastAPI/Docker/Kubernetes and monitored with MLflow. Also partnered with wealth advisors at Edward Jones to deliver a client retention model with SHAP-driven explanations and a dashboard that improved trust, adoption, and reduced high-value client churn.”

“Built and deployed a production compliance automation RAG system (at Citi) that generates citation-backed, schema-validated risk summaries for regulatory document review. Emphasizes regulated-environment reliability with retrieval-only grounding, abstention, confidence thresholds, and immutable audit logging, plus orchestration using LangChain/LangGraph and Airflow. Reported ~60% reduction in compliance review effort while maintaining high precision and traceability.”

“Built a production internal LLM-powered knowledge assistant using a RAG architecture (Python, LLM APIs, cloud services) that answers employee questions with sourced, grounded responses from internal documents. Demonstrates strong practical depth in retrieval tuning (chunking/metadata filters), orchestration with LangChain, and production reliability practices (latency optimization, automated embedding refresh, evaluation metrics, logging/monitoring) while partnering closely with non-technical operations teams.”

“Unity/C# mobile game engineer based in Belgrade who has owned and shipped complex meta-systems (shop/battle pass/tournaments) and real-time PvP using Photon, with a strong focus on performance (profiling, memory/GC, draw calls) and live stability. Notably handled a China launch by abstracting regional SDKs behind DI-based interfaces and using Addressables for localized assets, keeping the global codebase clean while maintaining high performance.”

Sai Vamsi

Screened

Mid-Level Software Engineer specializing in backend microservices and cloud-native systems

TX, USA4y exp

“ServiceNow engineer who built an AI-powered ticket summarizer end-to-end (RAG with vector DB + GPT, Redis latency optimizations, fallback summarization, and a React UI widget for agent feedback). Also has hands-on ROS 2 experience building real-time sensor-fusion nodes (LiDAR/IMU), debugging SLAM/navigation issues via rosbag + EKF tuning, and bridging heterogeneous robots by translating ROS 2 topics to MQTT/JSON. Strong DevOps background with Docker, Jenkins CI/CD, and Kubernetes orchestration for scalable deployments.”

ESHWANTH D. G

Screened

Mid-level Robotics Software Engineer specializing in autonomous perception and sensor fusion

CA, USA4y exp

“Robotics engineer with Honeywell and Tata Motors experience deploying ROS/ROS2 autonomous mobile robot fleets into live factory environments, integrating sensors, safety PLCs, and on-prem services. Known for solving end-to-end latency and stability issues (including network spikes under load) using gRPC, Docker, and improved diagnostics—cutting diagnosis time from hours to minutes and achieving sub-150 ms control response.”

Young Joon Suh

Screened

Senior Research Scientist specializing in AI for autonomous driving and semiconductors

Seoul, Korea5y exp

“Robotics perception engineer focused on autonomous driving 3D detection, integrating PETR embeddings into BEVFormer and tackling hard orientation/temporal alignment issues in multi-camera BEV pipelines. Uses Gazebo with custom sensor plugins to validate calibration, timing, and transforms, and blends synthetic labels with real imagery for scalable 3D box generation.”

Sri Harsha patallapalli

Screened

Mid-level Machine Learning & Data Infrastructure Engineer specializing in MLOps on AWS

Boston, MA5y exp

“Built and deployed a fine-tuned Qwen 2.5 14B model into production at Dextr.ai as the backbone for hotel-operations agentic workflows, running on AWS EKS with Triton and TensorRT-LLM. Demonstrates strong cost-aware LLM engineering (QLoRA, FP8/BF16 on H100) plus rigorous benchmarking/observability (Prometheus, LangSmith) with reported sub-30ms TTNT. Previously handled long-running ETL orchestration with Airflow at GE Healthcare and Lowe's.”

“Backend engineer who built an NL-to-SQL system at Target, using a multi-step LLM pipeline with vector-store schema retrieval and SQL validation to safely answer business questions. Strong in production FastAPI systems (async, Pydantic, Docker/Uvicorn, load balancing) and security (OAuth2/JWT, scopes, and database row-level security), with experience migrating Flask apps to FastAPI + PostgreSQL using strangler/feature-flagged canary rollouts.”

“AI/LLM engineer who built a production autonomous GenAI content ecosystem that generates short-form scripts, extracts viral highlights from long-form video, and dubs content into 33+ languages. Focused on making LLM outputs production-safe via schema enforcement, token-to-time alignment, critic-agent verification, and scalable async orchestration—cutting manual workflows by ~90% and saving $200k+ annually.”

“Built and deployed a production long-form article summarization system using BART/T5/PEGASUS, tackling real-world constraints like token limits, latency/quality tradeoffs, and factual drift via chunking/merge logic and constrained decoding. Uses pragmatic Python-based pipeline orchestration (scheduled jobs, modular scripts, logging/retries) and iterates with stakeholder feedback to make outputs genuinely useful for content workflows.”

“Embodied AI / robotics-focused ML engineer with experience at JPMorgan and EY building language-to-robot control systems that connect transformer/LLM intent to safe real-world robotic actions. Designed production-grade, low-latency architectures (Kafka/Redis, monitoring, CI/CD) and applied sim-to-real and model distillation to make research ideas deployable on physical systems.”