Vetted Amazon SageMaker Professionals

“Built and deployed a real-time hospital readmission risk prediction system at NYU Langone Health, combining structured EHR data with BERT-based NLP on clinical notes and serving predictions to clinicians via Azure ML and FHIR APIs. Emphasizes production reliability and clinical trust through SHAP-based explainability and robust healthcare data preprocessing, and reports a 22% reduction in 30-day readmissions.”

“ML engineer/data scientist who built and deployed a real-time fraud detection platform at Citi on AWS SageMaker, processing 3M+ daily transactions and improving fraud response by 28%. Combines unsupervised anomaly detection (autoencoders) with ensemble models (XGBoost/Random Forest) plus Airflow/Step Functions orchestration, drift monitoring, and explainability (SHAP) to keep models reliable and compliant in production.”

“Built and deployed a production RAG system for financial/compliance teams using GPT-4, Claude, and local models to retrieve and summarize thousands of internal documents with strong security controls (role-based retrieval, PII masking). Drove significant operational gains (30+ hours/week saved, ~35% productivity lift, ~45% faster responses) and orchestrated end-to-end ingestion/embedding/index refresh pipelines with Airflow, S3, and SageMaker while partnering closely with compliance stakeholders on auditability and traceability.”

“Backend/ML platform engineer who owns end-to-end delivery of ML-serving APIs (FastAPI + TensorFlow) and runs them reliably on Kubernetes using ArgoCD GitOps. Has hands-on experience solving production-only issues (probe tuning for model warm-up, resource profiling) and building scalable Kafka streaming pipelines, plus supporting phased on-prem to AWS migrations with dependency discovery and recreation of hidden jobs/workflows.”

“Built and deployed an AI agent to help patients navigate complex housing information by scraping and normalizing unstructured data across all 50 U.S. states, then layering a LangChain RAG system with MMR re-ranking to reduce hallucinations. Experienced in orchestrating multi-agent workflows (LangGraph/CrewAI) and production reliability practices (Pydantic-validated outputs, LLM-as-judge evals, tracing). Also delivered stakeholder-facing explainability via SHAP dashboards for a loan-approval predictive model at Welspot.”

“NLP/ML engineer with hands-on healthcare and support-ticket text experience, building clinical-note structuring and semantic linking systems using spaCy, BERT clinical embeddings, and FAISS. Emphasizes production-grade delivery (Airflow/Databricks, PySpark, Docker, AWS/FastAPI/Lambda) and rigorous validation via clinician-labeled datasets, retrieval metrics, and user feedback.”

“ML engineer/data scientist who deployed a production credit risk + insurance claims triage platform at Hartford Financial, combining XGBoost default prediction with BERT-based document classification. Demonstrated strong MLOps by cutting inference latency to sub-500ms and building drift monitoring plus automated retraining/deployment pipelines (MLflow, CloudWatch, GitHub Actions, SageMaker) with human-in-the-loop review and SHAP-based explainability for underwriting adoption.”

“ML/NLP practitioner with healthcare payer experience at HCSC, focused on connecting messy unstructured clinical notes to structured claims/provider data to improve fraud-analytics workflows. Has hands-on experience fine-tuning transformers in AWS SageMaker, building large-scale embedding search with FAISS, and implementing robust entity resolution using golden datasets, precision/recall calibration, and production monitoring for drift.”

“Software engineer with connected-car/automotive production experience who owned an end-to-end remote door lock/unlock feature and introduced unit testing (GTest) plus rig/simulator validation. Also built and productionized an AI-native AWS cloud cost assistant (Lex + GPT-based LLM + Lambda + RAG/vector DB) with guardrails and achieved 94% evaluation accuracy. Helped replace a third-party solution with an in-house build, saving the company ~€9M.”

SREEJA REDDY Konda

Screened

Mid-level AI/ML Engineer specializing in NLP, MLOps, and predictive analytics

Kentwood, MI6y exp

“AI/ML Engineer at Fifth Third Bank who has shipped production fraud detection and risk analysis systems combining ML models with LLM-powered insights/explanations, including real-time monitoring, drift detection, and automated retraining under regulatory explainability constraints. Also built a hybrid-retrieval internal knowledge-base QA system (+20% top-5 relevance) and delivered a customer support chatbot that reduced first response time by 30% through strong stakeholder collaboration.”

Bhavana Anna

Screened

Mid-level AI/ML Engineer specializing in fraud detection and Generative AI (RAG)

USA5y exp

“AI/ML engineer who has shipped production LLM and ML systems, including a RAG pipeline that ingested ~500k insurance/client documents to help adjusters answer questions faster and more consistently. Experienced in handling messy real-world document formats, tuning retrieval/chunking, and reducing latency via vector search optimization, precomputed embeddings, and caching. Also built orchestrated fraud-detection deployment workflows using AWS Step Functions and SageMaker, and partners closely with non-technical operations teams on NLP automation.”

sai Pavan

Screened

Mid-level AI/ML Engineer specializing in MLOps, NLP, and real-time ML pipelines

5y exp

“Built a production, real-time insurance claims document-understanding and fraud-detection pipeline using TensorFlow + fine-tuned BERT, deployed on AWS (SageMaker/Lambda/API Gateway) with automated retraining via MLflow and Jenkins. Addressed noisy documents and latency using augmentation and model distillation (3x faster), cutting claims ops manual review by ~50% and reducing fraudulent payouts.”

Teja Babu Mandaloju

Screened

Mid-level Data Scientist/MLOps Engineer specializing in NLP, GenAI, and cloud ML platforms

Chicago, USA5y exp

“AI/ML engineer who led production deployment of a multimodal (text/video/image) RAG system on GCP using Gemini 2.5 + Vertex AI Vector Search, scaling to 10M+ documents with sub-second latency and +40% retrieval accuracy. Strong MLOps/orchestration background (Kubernetes, CI/CD, Airflow, MLflow) with proven impact on reliability (75% fewer incidents) and deployment speed (92% faster), plus experience delivering explainable ML (XGBoost + SHAP + Tableau) to non-technical retail stakeholders.”

Aniruddha Chakravarty

Screened

Junior Software Engineer specializing in cloud infrastructure, observability, and full-stack systems

Remote2y exp

“Built and productionized a predictive maintenance system (predictEngineLife) estimating Remaining Useful Life for PW4000 turbofan engines from large-scale, noisy telemetry—emphasizing modular pipeline design, deterministic preprocessing, and strong observability/guardrails. Also has hands-on experience diagnosing multi-agent LLM customer-support workflows (schema/state issues, fallback paths, regression tests) and has led developer workshops (GDG Pune) while partnering with sales teams on technical discovery and POCs.”

Sai Bandaru

Screened

Mid-level Machine Learning Engineer specializing in fraud detection and LLM systems

Boston, MA6y exp

“At FiVerity, built and deployed a production LLM/RAG-based Information Gathering Tool for credit union fraud analysts that generates auditable investigation summaries from verified evidence. Focused on high-stakes constraints—hallucination prevention, cross-entity leakage controls, compliance/PII-safe monitoring, and latency—while also shipping customer-facing agentic workflows using CrewAI and LangGraph in close partnership with fraud and compliance stakeholders.”

“Data engineering/software development candidate who built a stock market pipeline and uses that project to demonstrate strong architectural thinking across Kafka, Spark, and Airflow. They stand out for a pragmatic approach to AI: using tools like Copilot, ChatGPT, LangChain, and AutoGen to accelerate development while maintaining human oversight, testing, and system-level decision making.”

“Entrepreneur/fintech product builder who raised a $100K pre-seed from ex-Google/Microsoft execs and built a real-time, direct-to-vendor bill pay micropayments platform. Previously helped scale Norton LifeLock to 1M users (2003) and also created Karma LA, a fraud-resistant, verified donation system (including VA veteran verification) aimed at improving trust and conversion in giving.”

“Backend/data engineer in healthcare who built an AWS-based clinical analytics platform from scratch (DynamoDB/S3/Airflow/dbt) with sub-second clinician query goals, 99.9% uptime, and HIPAA-grade controls (KMS encryption, IAM RBAC, audit trails). Also modernized ML delivery by replacing a manual 4-hour deployment with a 30-minute Docker/GitHub Actions CI/CD pipeline using parallel runs, parity testing, and rollback, and caught critical EHR data edge cases (date formats/timezones) that could have impacted patient care.”

“Built a production GPT-4/LangChain/Pinecone RAG “AI Copilot” at Northern Trust to automate financial report generation and analyst Q&A over internal structured (SQL warehouse) and unstructured policy data. Focused on real-world production challenges—grounding and latency—achieving major speed gains (seconds to milliseconds) via MiniLM embedding optimization and Redis caching, and implemented rigorous testing/evaluation with MLflow-backed metrics while aligning compliance and finance stakeholders for deployment.”

“LLM/agent engineer who built a production multi-agent research automation system using LangGraph (planner, retriever with FAISS, supervisor, evaluator) with structured outputs and citation tracking for traceable reports. Emphasizes reliability and operations—LangSmith-based observability, multi-level testing, hallucination mitigation, and latency/cost controls—plus prior experience as a Computer Vision Software Engineer at Deepsight AI Labs working directly with non-technical customers.”

“Graduate Assistant Intern at Friends University who built and deployed a GenAI-driven requirement understanding system that automates extraction and semantic grouping of technical requirements from large unstructured documents. Demonstrates strong LLM engineering rigor (golden datasets, regression testing, post-processing validation) and production-minded delivery using LangChain/LlamaIndex orchestration, FastAPI microservices, Docker, and cloud deployment.”

“Built and deployed a production LLM/RAG system at CVS to automate clinical documents, addressing PHI compliance, retrieval accuracy, and latency; achieved a 35–40% reduction in review effort through chunking and FP16/INT8 optimization. Also has experience translating AI outputs into actionable insights for non-technical stakeholders (sports analysts).”

“ML/AI engineer with production experience in high-scale banking fraud detection at Truist, building an end-to-end pipeline (Airflow/AWS Glue/Snowflake, PyTorch/sklearn) with automated retraining and Kubernetes-based deployment; delivered measurable gains (22% fewer false positives, 15% higher recall) and reduced manual ops ~40%. Also partnered with clinicians at Kellton to deploy an LLM system for summarizing/classifying clinical notes, improving review time and decision speed.”

“ML/NLP engineer with hands-on experience building production systems for unstructured insurance claims and customer data linking. Delivered measurable impact at scale (millions of documents), combining transformer-based NLP, vector search (FAISS/Pinecone), and human-in-the-loop validation, and has strong production workflow/observability practices (Airflow, AWS Batch, Grafana/Prometheus).”