Vetted SciPy Professionals

“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.”

“Backend/data engineer with production experience building secure Django/DRF APIs (JWT RS256 + rotating refresh tokens), background processing with Celery, and strong reliability practices (timeouts, retries/backoff, structured logging, audit trails). Has delivered AWS solutions spanning Lambda + ECS with IaC/CI-CD and built Glue/PySpark ETL pipelines with schema evolution and data-quality quarantine patterns; also modernized a legacy SAS pipeline to Python/PySpark with parallel-run parity validation and phased rollout.”

“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.”

Chandra Shekar Akkandra

Screened

Mid-level AI/ML Engineer specializing in fraud detection and risk analytics in Financial Services

Newark, CA5y exp

“Finance-domain ML/LLM engineer who has shipped production systems including a RAG-based financial insights assistant with a custom post-generation validation layer that verifies atomic claims against retrieved source text to prevent hallucinations in compliance-critical workflows. Also built large-scale MLOps automation on AWS using Kubeflow + MLflow + CI/CD for fraud detection and credit risk models processing 500M+ transactions/day with a 99.99% uptime goal, and partnered closely with JP Morgan risk/compliance stakeholders on NLP-driven compliance monitoring.”

Mukundan Sridharan

Screened

Executive Technology Leader (CTO) specializing in IoT sensing, AI/ML, and RF/embedded systems

Rockville, MD22y exp

“Currently a startup CTO who thrives on building new technology stacks and rapidly turning technical ideas into products. Interested in partnering with a CEO/business team to commercialize embedded/edge concepts such as multi-sensor drone localization (video/audio/RF with SDR), low-cost solar+battery power nodes networked via LoRa, and an Amazon Sidewalk/LoRa connectivity device with cloud management.”

Aniket Janrao

Screened

Junior Data Scientist specializing in healthcare ML and clinical NLP/LLMs

Houma, LA2y exp

“Healthcare-focused LLM engineer who has built two production clinical applications: an automated structured clinical report generator from physician-patient conversations and a RAG-based chatbot for retrieving patient history (procedures, allergies, etc.). Demonstrates strong applied RAG expertise (overlapping chunking, entity dependency graphs, temporal filtering, graph RAG) to reduce hallucinations/omissions and partners closely with clinicians to automate hospital workflows.”

Dhriti Kanchan

Screened

Mid-level Data Analyst specializing in healthcare and financial analytics

Texas, USA5y exp

“Analytics-focused candidate with hands-on experience turning messy CRM, e-commerce, payments, and support data into trusted reporting datasets using SQL and Python. They have owned end-to-end churn and retention analytics work, including RFM-based segmentation, dashboard delivery, and metric standardization across sales, marketing, and finance.”

“Backend-leaning full-stack engineer with 7 years of Python experience who has worked on data-heavy products in both healthcare and social media intelligence. Particularly notable for driving Elasticsearch-based search improvements on a B2B social media analytics platform and for building secure healthcare APIs using Flask/Django with OAuth, JWT, and multiple databases.”

“ML/GenAI engineer with recent CVS Health experience building a production RAG system over unstructured financial/research documents using LangChain, FAISS, and Pinecone, plus LoRA/PEFT fine-tuning of GPT/LLaMA for domain-aware summarization. Demonstrates strong applied MLOps and data engineering skills (Airflow/Prefect, Docker/Kubernetes, CI/CD, MLflow) and measurable impact (sub-second retrieval, ~40% better context retrieval, ~25% entity matching improvement).”

“Healthcare-focused data engineer/ML practitioner with experience at Lightbeam Health Solutions and Humana building production entity-resolution and semantic similarity pipelines across EMR, lab, and claims data. Uses NLP/ML (spaCy, scikit-learn, BioBERT/LightGBM) plus Snowflake/Airflow and vector search (Pinecone) to improve linkage accuracy (reported 90%) and semantic match quality (reported +12–15%), while reducing manual cleanup by 40%+.”

“Data engineer focused on reliable, scalable analytics pipelines and external data collection. Has owned end-to-end pipelines processing 5–10M records/day, serving Snowflake data marts to Power BI/Tableau, and reports ~99% reliability through strong validation/monitoring. Also shipped versioned REST APIs for curated data with query optimization and caching.”

Sampada shelke

Screened

Mid-level Machine Learning Engineer specializing in NLP, LLMs, and applied research

La Jolla, CA3y exp

“New grad SDE (AI/ML) who built and deployed an LLM-based chatbot framework used across technology, military, and banking contexts, focusing on model selection tradeoffs (latency vs accuracy) through prototyping and benchmarking. Also built a multi-agent "eaterybot" using PyAutoGen/AutoGen with a manager agent orchestrating specialized agents, and emphasizes rigorous testing with adversarial/edge-case datasets and hallucination checks.”

Kunal Kulkarni

Screened

Intern AI/ML Researcher specializing in computer vision and data engineering

Palo Alto, CA1y exp

“Built a production-oriented multimodal RAG "Fix Assistant" with FastAPI, Tavily search, BM25 + cross-encoder reranking, and a local Phi-3.5 model, emphasizing strict grounding and fallback/verification modes to prevent hallucinations. Also has hands-on federated learning experience using STADLE to orchestrate edge-node training and aggregation for EV telemetry data, plus experience communicating AI results to non-technical stakeholders (traffic RL/congestion outcomes).”

“AI Engineer with hands-on ownership of a production multi-agent RAG platform in financial services, spanning experimentation, architecture, deployment, monitoring, and iterative optimization. Stands out for measurable impact: 35% retrieval relevance improvement and nearly 50% reduction in manual operational analysis effort, plus strong experience making enterprise LLM systems safer and more reliable in production.”

“Full stack Python developer who actively integrates AI coding assistants into day-to-day engineering work, including code generation, debugging, testing, and documentation. Has also coordinated multi-agent workflows across backend, frontend, testing, and code review, showing an applied, productivity-focused approach to AI-enabled software delivery.”

“Entry-level software engineer with strong full-stack experience building React/TypeScript and Node.js analytics products, especially around performance optimization for large datasets. Stands out for combining hands-on engineering with user discovery, and for delivering measurable wins like 40% fewer API calls, page load improvements from 3.2s to 1.1s, and 70% faster PostgreSQL queries during an internship at Tapastry.”

Murali Marupudi

Screened

Mid-level Backend Engineer specializing in Python microservices and scalable systems

Jersey City, NJ4y exp

“Full-stack engineer with hands-on experience shipping both secure platform features and production AI systems. They combine React/TypeScript, Flask/Node.js, and PostgreSQL fundamentals with practical LLM and NLP implementation, including retrieval, schema-validated outputs, monitoring, and human-in-the-loop safeguards. Notable impact includes cutting manual review by 40% and reducing post-update error rates by over 20%.”

“AI engineer who has shipped production LLM systems across customer service and marketing use cases—building a RAG app on Azure OpenAI and speeding retrieval with Redis caching tied to Okta sessions. Also implemented a LangGraph multi-agent workflow that pulls image context from Figma to generate structured HTML marketing emails, adding a verification agent to improve image-selection accuracy while optimizing solution cost for business stakeholders.”

“Designed and deployed an enterprise LLM-powered clinical/pharmacy policy knowledge assistant at CVS Health, replacing manual searches across PDFs/Word/SharePoint with a HIPAA-compliant RAG system. Built end-to-end ingestion and orchestration (Airflow + Azure ML/Data Lake + vector index) with PHI masking, versioned re-embedding, and production monitoring (Prometheus/Grafana), and partnered closely with clinicians/compliance to ensure policy-grounded, auditable answers.”

“AI/LLM engineer with production experience building secure, scalable compliance-focused generative AI systems (GPT-3/4, BERT) including RAG over internal regulatory document bases. Has delivered end-to-end pipelines on AWS with PySpark/Airflow/Kubernetes/FastAPI, emphasizing privacy controls, monitoring, and iterative evaluation (A/B testing). Also partnered closely with bank compliance officers using prototypes to refine NLP summarization/classification and reduce document review time.”

“Space/heliophysics ML engineer who built a PyTorch GRU model to propagate solar wind from L1 to the magnetopause with probabilistic outputs for uncertainty quantification, achieving ~25% better CRPS than standard approaches. Also developed production-grade Python ETL and an open-source telemetry processing package for a mission (LEXI), using Docker and GitHub Actions CI/CD and iterating with scientist/engineer stakeholders.”

“ML/NLP practitioner who built customer-feedback topic modeling (NMF + TF-IDF) to diagnose chatbot-to-agent handovers and drove product/ops changes that reduced operational costs by 20%. Also developed LSTM-based intent recognition using Word2Vec/GloVe embeddings for semantic linking, and deployed an LSTM autoencoder for fraud anomaly detection that cut false positives by 25% while capturing 15% more fraud in A/B testing.”

Saicharitha Yanamandala

Screened

Mid-Level Software Developer specializing in Java, Cloud, and Microservices

Chicago, IL6y exp

“Backend/Python engineer who owned an end-to-end FastAPI + AWS internal natural-language document Q&A system (Textract extraction, embeddings/vector DB, LLM integration) with strong focus on reliability and latency. Hands-on with Kubernetes + GitOps (Argo CD, Helm, rolling updates/auto-rollback) and built/optimized Kafka streaming pipelines using Prometheus/Grafana. Also supported a zero-downtime on-prem to cloud migration with parallel run and gradual traffic cutover.”

Sharan Raj Sivakumar

Screened

Senior Software Developer specializing in AI/ML automation and cloud-native systems

New York City, NY6y exp

“ML/MLOps practitioner who built production systems for telecom network analytics, including an automated labeling + multi-label Random Forest solution that cut labeling effort by 90% and sped up RCA. Led an Ericsson auto-deployment platform using Airflow, Azure IoT Hub, Docker, and Celery to orchestrate 120+ containerized ML/rule-based deployments, saving ~80 hours of setup per deployment.”