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Vetted NumPy Professionals
Pre-screened and vetted.
Manas Agarwal
Junior Full-Stack Software Engineer specializing in Python APIs, React, and cloud AI integrations
“Customer-facing software engineer who builds and deploys practical AI/RAG solutions (e.g., an AI assistant for searching billing PDFs) by deeply understanding support workflows and iterating with users. Demonstrates strong production instincts—quickly stabilizing peak-traffic API timeouts with caching/background jobs, then implementing durable fixes with proper monitoring and maintainable code practices.”
Sree Manasa Vuppu
Mid-level AI Engineer specializing in Generative AI, LLMs, and RAG
“Internship at Discovery Education building a production LLM/RAG chatbot that let marketing and sales teams query and interpret Looker/BI dashboards in natural language, with responses grounded in compliance and state education standards. Emphasizes rigorous evaluation (faithfulness/precision/recall/latency) plus user-feedback analytics, and used LangChain for orchestration, chunking/context-window control, and integration with enterprise sources like SharePoint.”
Dylan Jaworski
Intern Software Engineer specializing in Python data pipelines and backend systems
“Software engineering intern at the Florida Department of Transportation who built validation/anomaly-detection logic for a live operational telemetry + system log processing pipeline. Emphasizes fault-tolerant, state-driven system design (degraded modes, data freshness tracking, safe fallbacks) and debugs time-sensitive behavior via logging/latency analysis and replay-based testing—skills that translate well to robotics-style architectures despite no direct ROS/robot experience.”
Ttanvi Tummapudi
Junior Data Scientist specializing in machine learning, predictive modeling, and applied AI research
“Data scientist/researcher who has built two multimodal LLM systems: an AI-assisted medical triage pipeline using GPT-4o vision + RAG with confidence-scored red/yellow/green outputs, and a master’s project on multimodal cyberthreat detection combining multiple models and using TinyLlama to generate human-readable risk reports. Also partnered with business analysts at Sanvar Technologies to deliver a churn prediction pipeline and Tableau dashboard for decision-making.”
Reenam Jot Kaur
Junior Business Operations & Systems Analyst specializing in automation, QA, and analytics
“Operational/data-focused QA professional who applies manufacturing-style quality gates to supplier workflows, owning supplier data quality from onboarding through billing readiness. Built automated validation and variance-threshold checks (Tableau/Excel-driven monitoring) and partnered directly with suppliers to resolve pricing discrepancies early, preventing large-scale transaction and billing issues.”
Rohith Reddy Aleti
Mid-level Data Scientist specializing in AI, analytics, and predictive modeling
“Data analytics and BI professional with experience turning messy institutional and customer data into decision-ready reporting and predictive systems. They combine strong SQL/Python execution with end-to-end ownership of churn analytics, stakeholder alignment, and operational rollout into dashboards and CRM workflows.”
Visswanath Baskaran
Mid-level Data Analyst specializing in BI, ETL, and forecasting
“Analytics professional with hands-on experience at Charge Dock building SQL- and Power BI-based operational reporting for SLA compliance, uptime, and incident management. Also developed a reproducible Python demand forecasting workflow using Pandas and Statsmodels to support inventory planning, showing a blend of BI, data engineering, and forecasting capability.”
Hashim Kalam
Junior Software Engineer specializing in backend systems and applied AI
“Full-stack and backend-oriented engineer with experience spanning customer-facing web products, LLM-based multi-agent systems, and enterprise Go microservices. They’ve worked on a ticketing/payments platform with Stripe, an NLP-to-2D-CAD MVP, and AI orchestration using LangChain, OpenAI, and Gemini, showing a mix of product thinking, backend architecture, and applied AI experimentation.”
SAUD SALEEM
Senior Python Engineer specializing in scalable backend and distributed systems
“Startup-oriented full-stack engineer who has worked across distributed systems, data, and compliance-heavy B2B SaaS products. They led an incremental migration from a monolith to an event-driven microservices architecture and also built high-volume healthcare ETL pipelines, giving them a strong mix of product engineering, platform modernization, and data-intensive backend experience.”
Karthik Patralapati
Mid-level AI/ML Software Engineer specializing in GPU-optimized LLM inference and cloud microservices
“Built and deployed a production RAG-based multilingual analytics assistant for healthcare operations, enabling non-technical teams to query claims/EHR and risk metrics with grounded explanations. Demonstrates strong end-to-end LLM system engineering (retrieval tuning, re-ranking, hallucination controls, verification layers) plus workflow orchestration (Airflow/Composer/Step Functions) and stakeholder-driven iteration via prototypes and dashboards.”
Baby Swathi Ramisetti
Mid-level Backend Software Engineer specializing in Python APIs and cloud-native systems
“Software/product engineer who owns customer-facing internal platforms end-to-end, with deep experience building data pipeline health and data quality tooling (near-real-time alerting and ops dashboards). Strong in React/TypeScript + Python REST architectures and microservices with RabbitMQ, emphasizing reliability patterns (idempotency, DLQs, correlation IDs) and fast, safe iteration via feature flags, testing, and observability.”
Shram Kadia
Junior Software Engineer specializing in ML, RAG systems, and safety-critical risk modeling
“Backend/cloud engineer from Resilient Tech with hands-on experience deploying REST APIs and database migrations into a live ERP used by real customers while maintaining 99% uptime. Has debugged intermittent AWS container timeouts down to security group/load balancer misconfigurations, and has extended Python in an ERPNext system to meet GST/e-invoicing compliance requirements with strong customer collaboration.”
Ashish Dnyaneshvar Parulekar
Mid-Level Software & Machine Learning Engineer specializing in cloud-native microservices and LLMs
“Backend engineer who owned the API layer for an AI trust/analytics dashboard (trust scores, stability checks, public verification endpoints) using Python/FastAPI and Postgres. Has hands-on DevOps experience deploying FastAPI and Node.js services to AWS Kubernetes with GitHub Actions + ArgoCD GitOps, plus Kafka-based real-time event streaming and careful staged migration practices (shadow traffic/dual writes, rollback planning).”
Bhavana Polakala
Intern Data Scientist specializing in GenAI agents, RAG, and ML platforms
“LLM/agent systems builder who deployed a production hybrid router for immerso.ai that dynamically selects retrieval vs reasoning vs generative pathways, achieving an 82% factual-accuracy lift. Deep hands-on experience optimizing local Mistral 7B inference (4–5 bit GGUF quantization, KV-cache reuse) and building reliable RAG/agent workflows with LangChain/LangGraph/AutoGen across GCP Cloud Run and AWS (ECS/Lambda).”
Vamsi Krishna
Senior Machine Learning Engineer specializing in MLOps and Generative AI
“Built and deployed a production generative-AI copilot at Tungsten that automates invoice/form extraction template creation, reducing weeks of manual model-building work. Combines fine-tuned LLMs (PyTorch/HuggingFace) with OpenCV layout grounding to reduce hallucinations, and runs an end-to-end Kubeflow-based MLOps pipeline with drift monitoring, canary releases, and automated retraining.”
Doondi Ashlesh Tammineedi
Junior Full-Stack Software Engineer specializing in cloud-native web apps and AI tooling
“Software engineer with experience across edtech, live gaming, and an AI document intelligence platform, delivering end-to-end customer-facing features and production backends. Built secure, automated live-session scheduling integrating Zoom and TalentLMS (JWT/RBAC, idempotency, transactions) cutting setup time from ~3 minutes to under 1 minute, and optimized real-time gaming dashboards/APIs with query tuning, caching, and CDN improvements (~60% latency reduction under peak load) on AWS.”
BHAVANA KRISHNAN
Junior AI/ML Engineer specializing in Generative AI, NLP, and MLOps
“LLM engineer who has deployed a production RAG system (LangChain/FAISS/FastAPI) for enterprise semantic search, tackling real-world latency by LoRA/PEFT fine-tuning and grounding outputs with retrieval. Brings strong MLOps (Docker, AWS EKS, CI/CD, MLflow) plus stakeholder-facing explainability experience using SHAP to align ML-driven financial guidance with non-technical domain experts.”
Pranav Mishra
Junior Machine Learning Engineer specializing in LLM agents, RAG, and MLOps
“AI/ML engineer who has shipped production systems across computer vision and conversational agents: built a YOLOv8-based wheel fitment pipeline at a Techstars-backed automotive startup, focusing on sub-second latency, monitoring, and robust fallback mechanisms that drove 2–3x page view growth and +5–6k users. Also built a voice-based interview platform orchestrating Deepgram + GPT-4 Mini + OpenAI TTS with FSM-driven reliability, and has hands-on RAG experience (LangChain, hybrid retrieval, cross-encoder reranking, custom pseudo-query generation).”
Rethvick Sriram Yugendra Babu
Junior AI/ML Software Engineer specializing in Generative AI and scalable data pipelines
“Built and operated large-scale biodiversity/ecological research platforms, integrating 50+ heterogeneous global datasets into a unified BIEN 3 schema on PostgreSQL/PostGIS and improving data consistency by 35%. Strong production engineering background (Linux monitoring, CI/CD performance gates, Docker on AWS/Azure) plus applied AI work building a Python RAG system (0.90 precision) and halving latency with Elasticsearch.”
Taruni Reddy Ampojwala
Mid-level GenAI Engineer specializing in LLM agents and RAG systems
“Built and deployed a production RAG-based LLM assistant that answers day-to-day operational questions from internal PDFs/SOPs, with strong emphasis on data consistency (metadata versioning, confidence thresholds, conflict handling) and low-latency retrieval at scale. Experienced designing and orchestrating multi-agent LLM workflows (retrieval/validation/generation) and pipeline orchestration for ingestion/embedding/vector-store updates, plus iterative delivery with non-technical operations/business stakeholders.”
MANOGNA VADLAMUDI
Junior AI Engineer specializing in LLM evaluation, prompt engineering, and AI orchestration
“LLM workflow builder who has deployed a personalized GPT experience (including Delphi AI-based knowledge ingestion) and built a LangChain/LangGraph job-aggregation pipeline that ingests, normalizes/dedupes, filters, then uses an LLM to rank and summarize matches. Emphasizes production reliability with structured outputs, retries/fallbacks, metric-driven evaluation, logging/prompt versioning, and A/B testing, and collaborates with non-technical stakeholders through demo-driven iteration.”
Yashwant Gandham
Junior Machine Learning & Backend Engineer specializing in LLM systems and ML infrastructure
“Built and deployed production RAG-based document search/Q&A systems (DocChat and an internship marketing RAG), using a React + FastAPI stack on GCP with docs stored in GCP buckets and retrieval via embeddings/vector DB. Emphasizes cost/performance tradeoffs (reported ~40% cost reduction) and ships via Docker (Railway), with load/API testing using JMeter and Swagger; regularly collaborates with a CEO stakeholder to iterate and push changes to production.”
Varun Mahankali
Junior Full-Stack Software Engineer specializing in React, Node.js, AWS, and Generative AI
“Built and production-deployed a Streamlit-based PDF RAG chatbot using LangChain (FAISS, embeddings, prompt templates) and OpenAI, optimizing Streamlit’s stateless behavior by caching vector DB + chat history to cut latency and API cost. Demonstrates a rigorous evaluation mindset (gold datasets, unit tests, LLM-as-judge, groundedness KPIs) and has experience communicating privacy/accuracy safeguards (RBAC, data masking, citations) to a non-technical client at Kalven Technologies.”
Srikar Tharala
Mid-level AI/ML Engineer specializing in Generative AI and RAG systems
“Currently at ProShare and reports building an AI/LLM-powered system deployed to production, aimed at helping with status-related difficulties and reducing misunderstandings across transactions. Also cites prior collaboration at Porsche with marketing teams, focusing on translating marketing goals into technical requirements and communicating solutions clearly to non-technical stakeholders.”