Vetted Keras Professionals

“Built and deployed a GPT-4-powered medical assistant for clinical staff to reduce time spent searching guidelines and EHR information, with a strong emphasis on safety and compliance. Uses strict RAG, confidence thresholds, and fallback behaviors to prevent hallucinations, and runs production-grade workflows orchestrated with LangChain/LangGraph plus Docker/Kubernetes/MLflow and monitoring for reliability and cost.”

“LLM/ML engineer who has shipped an enterprise RAG-based Q&A system (LangChain/LlamaIndex, FAISS + Azure Cognitive Search, GPT-3.5/4 via OpenAI/Azure OpenAI) to production on Docker + Kubernetes/OpenShift, tackling hallucinations, retrieval quality, latency/cost, and RBAC/IAM security. Also partnered with operations leaders to turn manual reporting into an LLM-powered summarization and forecasting dashboard driven by real KPIs and iterative stakeholder feedback.”

“Cloud-native integration engineer (Oracle/OCI) with strong production deployment and incident-response experience, including API gateway rollouts, observability (Prometheus/Grafana), and multi-layer debugging for payments systems. Built Python/FastAPI microservices and automation for customer-specific reporting and data sync, and has delivered major performance gains (45 min to <10) plus reliability improvements (MTTD reduced 40%+) through monitoring, playbooks, and resilient integration patterns (streaming/queuing, retries, secure tokens, VPC peering).”

“LLM/agentic workflow specialist with healthcare deployment experience who has taken LLM-based automation from prototype to production using operator-in-the-loop validation, RAG-style retrieval, RBAC, and monitoring for sensitive data compliance. Demonstrated real-time incident resolution (retrieval timeouts due to network/proxy misconfig) and strong GTM support—hands-on developer workshops and sales demos translating technical safeguards and real-time ETL into measurable ROI (70% ops reduction, ~$200K/year savings).”

“ML/NLP engineer who built a RAG-based technical assistant for Caterpillar field engineers, transforming PDF keyword search into intent-based semantic retrieval across manuals, logs, sensor reports, and technician notes. Strong in productionizing data/ML systems (Airflow, PySpark) with rigorous preprocessing, entity resolution, and evaluation—delivering measurable gains in accuracy, relevance, and duplicate reduction.”

“Backend/AI engineer who built a Spring Boot timesheet API on AWS (Postgres, Docker, Nginx) used by hundreds of daily users and resolved severe deadline-driven latency/5XX incidents via query optimization, connection pool tuning, and Redis caching. Also shipped application-layer LLM features (Mistral + LangChain chatbot) and designed a Planner/Executor/Verifier troubleshooting agent with verification-based guardrails to prevent hallucinated root-cause analyses.”

“AI/ML engineer at Cigna Healthcare building a production, HIPAA-compliant LLM-powered clinical insights platform that summarizes unstructured medical notes using a fine-tuned transformer + RAG on AWS. Demonstrates strong end-to-end MLOps and cloud optimization (distillation, Spot/Lambda/Auto Scaling) with quantified outcomes (~28% accuracy lift, ~40% less manual review, ~25% lower ops cost) and strong clinician-facing explainability via SHAP and dashboards.”

“Currently at Huntington Bank, built a production-grade RAG system that helps business/operations teams get grounded answers from large volumes of internal enterprise documents. Owns ingestion and FastAPI backend, tuned hybrid BM25+vector retrieval and chunking for relevance, and evaluates reliability with metrics and observability (LangSmith, CloudWatch, Prometheus/Grafana) while partnering closely with non-technical stakeholders.”

Cristian Vega

Screened

Senior AI/ML Engineer specializing in Generative AI and RAG

California, null9y exp

“ML/NLP practitioner at Morf Health focused on unifying fragmented healthcare data by linking structured patient/encounter records with unstructured clinical notes. Has hands-on experience with transformer embeddings, vector databases, and domain fine-tuning, plus rigorous evaluation (precision/recall) and human-in-the-loop validation with clinical SMEs to make pipelines production-grade.”

Prithviraju Venkataraman

Screened

Mid-level AI/ML Engineer specializing in MLOps, NLP, and Computer Vision

Long Beach, CA5y exp

“Built and deployed a production LLM-powered text extraction/classification system that converts messy unstructured reports into searchable insights, running on AWS SageMaker with automated retraining and monitoring. Strong in orchestration (Step Functions/Kubernetes/Airflow patterns) and reliability practices (gold datasets, prompt/tool unit tests, shadow/canary/A-B testing, guardrails/rollback), and has experience translating non-technical stakeholder needs into an NLP workflow plus dashboard.”

shivapriya pillalamarri

Screened

Mid-level AI/ML Engineer specializing in financial analytics and production ML systems

Boston, MA4y exp

“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.”

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.”

“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.”

“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%.”

“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.”

“Master’s CS candidate with backend internship experience modernizing live operational workflows at NatWest/NetWess, focusing on reliability improvements, safer CI/CD deployments, and incremental refactors using feature flags and rollback paths. Built FastAPI-based APIs with strong security patterns (JWT + 2FA/TOTP, centralized authorization, RLS) and demonstrated attention to edge cases like idempotency and data consistency in a Netflix-clone project.”

“Verizon engineer who productionized an LLM-based personalization capability for a customer-facing digital platform, owning the path from success metrics through scalable APIs, A/B validation, and post-launch monitoring (latency/accuracy/drift). Experienced in diagnosing and fixing real-time LLM/RAG workflow issues under peak load, and in enabling adoption via tailored technical demos/workshops and sales support materials.”

“Built and productionized a HIPAA-compliant LLM+RAG Clinical AI assistant at Optum, fine-tuning GPT/LLaMA on de-identified patient notes and integrating FAISS/Pinecone for sub-second retrieval; reported to cut diagnosis time by ~20 minutes per case. Experienced in orchestrating ML pipelines (Airflow, AWS Step Functions, Azure Data Factory) and in reliability techniques for LLM systems (grounding, citations, confidence filters, monitoring) while partnering closely with clinicians and compliance teams.”

“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.”

“Accenture data/ML practitioner who deployed a retail churn prediction and BERT-based sentiment analysis system to production, integrating behavioral + feedback data and operationalizing it with ETL automation, orchestration, and CI/CD. Experienced managing 2TB+ multi-source data, monitoring drift in Databricks, and translating results into Power BI dashboards for marketing teams (including K-means customer segmentation).”

“Built and shipped a production browser automation LLM agent with a structured 4-stage workflow (plan/browse/extract/verify), emphasizing reliability via schema validation (Pydantic), constrained tool use, and contextual retry loops. Reports ~60% accuracy on the WebArena benchmark and monitors runs via console output and the Agno framework GUI, prioritizing accuracy over speed.”

Jaideep bommidi

Screened

Senior ML Engineer & Data Scientist specializing in LLM agents, retrieval/ranking, and MLOps

Denton, TX8y exp

“Machine Learning Engineer currently at Webster Bank building an enterprise-scale LLM agent for Temenos Journey Manager/Maestro, using RAG-style multi-stage retrieval with FAISS/Pinecone, hybrid dense+sparse search, and LoRA fine-tuning optimized via NDCG/MAP and A/B testing. Previously handled messy incident/telemetry data at Deuta Werke GmbH with deterministic + fuzzy entity resolution, and has strong production data engineering experience across Spark/Hadoop and Python ETL systems.”

Uday kumar swamy

Screened

Senior Machine Learning Engineer specializing in MLOps and NLP/GenAI

Chicago, USA9y exp

“Built a production LLM-agent framework for a startup that performs daily financial/trading analysis by combining live market data with internal tools, including a centralized memory module to prevent context drift and reduce hallucinations. Also implemented an Airflow-orchestrated retail price forecasting pipeline deployed to AWS endpoints, scaling parallel workloads via Kubernetes Executor and validating systems with rigorous functional + LLM-specific metrics and cross-team collaboration.”

Utkarsh Gupta

Screened

Junior Robotics & ML Engineer specializing in perception, navigation, and VLA models

Los Angeles, CA1y exp

“Robotics software engineer with hands-on AGV/AMR experience at ERIC Robotics, building ROS2-based LiDAR perception and localization on NVIDIA Jetson for real-time deployment. Improved unstable localization in challenging environments (e.g., tunnels/bushes along rail tracks) via scan-matching, filtering, and consistency checks, and cut latency by moving from rclpy to rclcpp and leveraging CUDA. Comfortable across the stack from simulation (MuJoCo/Isaac Sim/Gazebo, domain randomization) to deployment tooling (Docker, basic CI) and distributed ROS2/DDS systems.”