Vetted Amazon SageMaker Professionals

Rubeena Riyas

Screened References

Senior AI Engineer specializing in LLM agents, RAG, and scalable data platforms

7y exp

“ML/data engineer who owned an end-to-end production sales analytics pipeline at 15,000+ user scale, delivering ~50% compute reduction, ~80% faster reporting, and ~$1.2M impact. Also shipped a production RAG-based AI assistant over internal BigQuery/docs with evaluation metrics and safety guardrails, and built shared Python libraries to standardize reliability and accelerate engineering teams.”

“At PwC, built and productionized an agentic RAG enterprise search assistant over 6M internal documents (8M embeddings), deployed across AWS and GCP. Drove major retrieval gains (72%→92% precision via BM25+dense hybrid with RRF and cross-encoder re-ranking), reduced hallucinations 30%, achieved <2s latency at 50–60K queries/month, and cut support tickets 30%—boosting adoption to 2,500 users by adding source-cited answers.”

“Built and owned an end-to-end agentic RAG chatbot platform for Baptist Health that helped clinicians access policy and clinical documents faster, reducing manual lookup by 80% and delivering about $2M in annual savings. Brings strong healthcare GenAI production experience, including HIPAA-aligned governance, PHI redaction, observability, evaluation, and scalable Python/Kubernetes deployment practices.”

“Built and deployed a production RAG-based LLM Q&A and summarization platform for internal documents, emphasizing grounded answers with structured prompting and citations to reduce hallucinations. Experienced orchestrating end-to-end LLM workflows with LangChain plus cloud pipelines (Azure ML Pipelines, AWS), and runs iterative evaluation using both metrics (accuracy/hallucination/latency/cost) and real user feedback to drive reliability.”

“Cloud-focused DevOps/Infrastructure engineer with hands-on AWS high availability, migration cutovers, and production automation. Built Jenkins-based CI/CD pipelines (Git, SonarQube, Artifactory) and manages Terraform IaC with S3/DynamoDB remote state, PR-based reviews, and staged environment promotion; targets $160k base. No direct IBM Power/AIX/PowerHA experience.”

“Industrial AI/ML practitioner with experience deploying real-time monitoring and anomaly detection in a regulated Sanofi vaccine manufacturing facility, including root-cause workflows, logging/alerting, and SOP-aligned validation—achieving ~90% faster anomaly detection. Also built Python/NLP-style automation to accelerate instrumentation & control documentation (~40% faster) and delivered end-to-end predictive analytics for an agri-food operations/distribution client using close operator and leadership feedback loops.”

“FinTech/AI engineer who has shipped an end-to-end discrepancy-detection product for financial managers using Next.js, FastAPI/GraphQL, Pinecone, and AWS (with dev/staging/prod, observability, A/B testing, and documentation). Also built an AI-native “AI Genesis” system with agentic cyclic workflows, routing, and tool use, and has experience modernizing legacy systems via the strangler fig pattern while coordinating with senior stakeholders on a 5G autonomous simulation platform.”

Ayush Gupta

Screened

Mid-level AI Engineer specializing in Agentic AI and Generative AI

6y exp

“Built and deployed a live LLM-powered platform that takes a LinkedIn job URL + resume and generates job-specific resumes and personalized outreach at scale, with production-grade logging/monitoring/retries on Vercel + Railway. Experienced with agent orchestration (AWS Bedrock/Strands, LangGraph, CrewAI) and rigorous AI workflow testing, plus stakeholder-facing prototypes like data lineage/metadata and NL-to-SQL + dashboard generation.”

Priyanshu Maurya

Screened

Mid-level Data Scientist specializing in insurance, finance, and healthcare analytics

New York, NY3y exp

“Built and productionized LLM-driven sentiment scoring for earnings call transcripts at Goldman Sachs, replacing legacy NLP to deliver a cleaner trading signal while managing latency/cost via batching, caching, and distilled models. Also implemented an Airflow-orchestrated fraud modeling pipeline at MetLife with drift-based retraining and SageMaker deployment, and has a disciplined evaluation/rollout framework for reliable AI workflows.”

“ML engineer with production experience across healthcare and fraud domains, including end-to-end ownership of a telecare patient deterioration system at Oracle Health and a GPT-4/RAG fraud reporting solution at Cognizant. Stands out for combining scalable data/ML infrastructure, clinical NLP, and GenAI delivery with measurable gains in model quality and workflow efficiency.”

“Healthcare ML/AI engineer at Cigna who has owned a clinical RAG pipeline from prototype through production, monitoring, compliance, and iteration. Stands out for combining LLM product delivery with healthcare-grade safety and explainability, driving a 38% retrieval precision gain, 42% hallucination reduction, and meaningful improvements in team velocity and system reliability.”

“Applied LLMs and a graph-RAG architecture in Neo4j to automate an accounting firm's cross-checking of transactional books against tax regulations, indexing 1,000+ pages into a knowledge graph with vector search. Combines agentic LLM workflows with classical NER (Hugging Face/NLTK) and validates using expert-labeled held-out data plus precision/recall and measured accountant time savings after deployment.”

“20-year technology builder with ~8 years in healthcare AI, currently at Keycentrix modernizing a legacy pharmacy solutions business. Shipped an OCR MVP within days and delivered a rebate-based product generating ~$50K/month, leveraging Claude/LangGraph agentic automation to replace work typically requiring a much larger engineering team. Developing a "Longevity AI Copilot" B2B platform that synthesizes research, labs, and wearable data into personalized longevity protocols for HNW and corporate wellness markets; concept validated but not yet incorporated or funded.”

Pratik Jaiswal

Screened

Mid-level AI/ML Engineer specializing in financial services ML and MLOps

Remote, USA4y exp

“ML engineer/data scientist with M&T Bank experience who built a production reinforcement-learning portfolio analytics tool for wealth management, emphasizing near real-time performance via batch/serving separation and robust generalization through stress-scenario backtesting and RL regularization. Strong MLOps background (Airflow, Grafana, MLflow) and proven ability to drive adoption with non-technical stakeholders using KPI alignment and SHAP-based explanations.”

“Data engineer who has owned end-to-end production pipelines ingesting ~500GB/day from APIs/databases/Kafka into an S3 data lake (Glue/Spark) with Airflow-orchestrated Great Expectations quality gates. Built resilient external data collection systems with idempotent jobs, exponential-backoff retries, raw data capture, and backfills; also shipped Snowflake-backed APIs with caching, versioned endpoints, and backward-compatible data contracts. Led an early-stage Azure data platform build with phased delivery and GitHub Actions CI/CD, resolving schema-mismatch incidents quickly without downstream corruption.”