.png){kind=logo}
Vetted NumPy Professionals
Pre-screened and vetted.
Yash Sanap
Junior Data Scientist specializing in ML, geospatial analytics, and LLM applications
“Built and deployed a production AI “term explainer” agent that adapts explanations to beginner/intermediate/expert users by combining multi-step LLM reasoning with grounded Wikipedia retrieval. Owns end-to-end agent orchestration (smolagents/Python), reliability patterns (fallback across LLM providers, retries, guardrails), and observability/metrics-driven evaluation; also partnered with a non-technical researcher to deliver a plain-language research assistant agent.”
Vaishnavi K
Mid-level AI/ML Engineer specializing in GenAI, MLOps, and anomaly detection
“LLM/MLOps engineer who has shipped a production RAG-based technical documentation assistant (FastAPI) cutting manual review by 45%, with deep hands-on retrieval optimization in Pinecone/LangChain (HNSW, hybrid + multi-query search, caching). Also brings healthcare domain experience—building Airflow-orchestrated EHR pipelines and delivering FDA-auditability-friendly predictive maintenance solutions using SHAP/LIME explainability surfaced in Power BI.”
Rupesh Pathak
Junior Data Scientist and Robotics Perception Engineer specializing in GenAI and autonomous systems
“Robotics software architect who built an automated pick-and-place palletizing prototype at BLACK-I-ROBOTICS, spanning perception (multi-RealSense fusion, segmentation, 6D pose, ICP), GPU-accelerated motion planning (MoveIt 2 + NVIDIA CuRobo), grasp generation, and safety (human detection + safe mode). Also brings cloud/CI/CD depth from VERIDIX AI (AWS Cognito/Lambda/ECS and CodePipeline stack) and demonstrated strong debugging chops by reducing outdoor rover EKF drift to ~5 cm via Allan variance-based IMU tuning.”
Aman Jain
Mid-level Software Engineer specializing in cloud-native data pipelines and ML platforms
“Backend engineer who has owned end-to-end delivery of Python/FastAPI microservices for real-time data processing and alerting, including performance tuning (Postgres optimization, caching, async processing). Strong DevOps/GitOps background: Docker + Kubernetes deployments with GitHub Actions CI/CD and ArgoCD-driven GitOps, plus experience supporting phased on-prem to AWS migrations and building Kafka-based streaming pipelines.”
Yash Pankhania
Mid-level AI Engineer specializing in LLMs, RAG, and data engineering
“AI Engineer Co-Op at Northeastern University who built a production Patient Persona Chat Bot to help nursing students practice clinical interactions, fine-tuning Llama 3 and integrating a LangChain + Pinecone RAG pipeline deployed on Amazon Bedrock. Emphasizes clinical accuracy and reliability with guardrails, retrieval filtering, and continuous evaluation, and also brings strong data engineering/orchestration experience (Airflow, EMR/PySpark, ADF, dbt, Databricks, Snowflake).”
Krishna Rajput
Mid-level AI Engineer & Data Scientist specializing in LLMs, RAG, and multimodal systems
“LLM/GenAI engineer who built a production AI-powered credit risk policy summarization and compliance alerting platform at HCL Tech, focused on factual accuracy and auditability for a financial client. Implemented a multi-retriever LangChain RAG architecture with citations-only prompting, fallback agents, and human-in-the-loop legal review—cutting manual review time by 35% and scaling to 12 teams.”
nitesh bommisetty
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered solutions
“AI/NLP-focused practitioner who built a zero-/few-shot LLM event extraction system on the long-tail Maven dataset, combining prompt-structured outputs with LoRA/QLoRA fine-tuning and rigorous F1 evaluation. Also implemented entity resolution/data cleaning pipelines and embedding-based semantic search using Sentence-BERT + FAISS, and has healthcare experience delivering a multilingual speech/translation mobile prototype using HIPAA-compliant Azure Cognitive Services.”
saran palle
Mid-level Applied AI Engineer specializing in agentic LLM workflows
“AI engineer with production experience building a LangGraph-based, stateful multi-agent system at MetLife to automate complex insurance claims adjudication, integrating document discovery, Azure Document Intelligence OCR/extraction, and health data analysis. Strong in agent orchestration and production deployment (Docker + FastAPI REST APIs), with a structured approach to reliability, evaluation, and stakeholder-driven requirements.”
Drashti Patel
Junior Software Engineer and ML Researcher specializing in full-stack and applied deep learning
“LLM engineer who built a production-style educational questionnaire generation system (MCQs/fill-in-the-blanks/short answers) using Hugging Face models (BERT/T5) and implemented grounding, decoding tuning, and post-generation validation to control hallucinations and quality. Also developed a "tech care" assistant chatbot with a custom Python orchestration/router layer (intent classification, context management, multi-step flows) and a structured testing/evaluation approach including expert review and automated checks.”
Varun Vekaria
Junior Full-Stack Software Developer specializing in AI-powered web and health applications
“Built and launched “Language AI,” a Next.js/TypeScript app that clones a user’s voice (via ElevenLabs) to deliver language lessons in their own voice, using Supabase for auth/Postgres/storage and hosting backend on Render. Post-launch, identified ElevenLabs voice-clone limits after initial users and reworked the pipeline to store audio assets and delete clones to support more concurrent users; also added Google auth to improve adoption. Previously worked in a high-growth startup environment (Study Park) taking concepts from ideation to production.”
Mohith Venkata
Mid-level Full-Stack Developer specializing in cloud-native APIs and data workflows
“Built and owned end-to-end ordering and inventory/order management systems for a wholesale distributor, delivering an MVP quickly and iterating based on direct observation of daily users. Experienced with TypeScript/React + Node.js layered architectures and microservices using RabbitMQ, including real-world scaling issues (duplicates, backpressure) and observability practices (correlation IDs, structured logging).”
Santhoshi Priya Sunchu
Mid-level Data Scientist specializing in NLP and predictive modeling
“AI/ML practitioner in healthcare/insurance (Blue Cross Blue Shield) who built and deployed a production NLP system to classify patient risk from unstructured clinical notes. Experienced in end-to-end pipeline orchestration (Airflow, AWS Step Functions/Lambda/SageMaker) and real-time optimization (BERT to DistilBERT on AWS GPUs), with strong clinician collaboration to drive adoption.”
Ramya Sree Kanijam
Mid-level Software Engineer specializing in LLM, RAG, and cloud AI
“Recent master’s graduate who led a team project building an LLM-based chatbot with RBAC-controlled information disclosure and a focus on reducing hallucinations. Also has hands-on embedded robotics experience (Arduino obstacle-avoiding robot using ultrasonic sensors) and practical DevOps/cloud deployment exposure with Docker, Terraform, Jenkins, and AWS (EKS/ECS/CodePipeline).”
Jitesh Kumar S
Junior Machine Learning Engineer specializing in NLP, computer vision, and MLOps
“ML/LLM engineer with Meta experience building production AI systems for near real-time user-report classification and summarization under strict latency (<250ms), safety, cost, and privacy constraints. Has hands-on MLOps/orchestration experience (Airflow, Spark, MLflow, Kubernetes, Docker, GitHub Actions) plus observability (Prometheus/Grafana) and applies rigorous evaluation, staged rollouts, and A/B testing to keep agent workflows reliable in production.”
Surya Danturty
Intern AI/ML Engineer specializing in computer vision and time-series forecasting
“Undergrad who built a production RAG chatbot for a messy college website using OpenAI embeddings + FAISS, overcoming hard-to-crawl/non-selectable site content and strict API budget limits. Applies information-retrieval best practices (section-based chunking with overlap, precision/recall evaluation) and reliability techniques (edge-case testing, similarity thresholds, fallback responses), and has experience scaling similar indexing work to ~300,000 Wikipedia pages.”
Butchi Venkatesh Adari
Mid-level Machine Learning Engineer specializing in LLM platforms and robotic perception
“Built and shipped a production multi-agent personal financial assistant at AlphevaAI on AWS ECS, combining FastAPI microservices, Redis/SQS orchestration, and Pinecone-based hybrid RAG (semantic + BM25) to ground financial guidance. Improved routing accuracy with an embedding-based SetFit + logistic regression intent classifier feeding an LLM router, and optimized UX with live streaming plus cost controls via model tiering and caching.”
Shabari Vignesh
Mid-level Data Engineer specializing in cloud data platforms and AI agents
“Data/Backend engineer who has owned end-to-end merchant analytics systems on AWS: orchestrated multi-source ingestion (FISERV/Shopify/Clover) with Step Functions/Lambda, enforced strong data quality gates, and served curated datasets via Redshift and a FastAPI layer. Also built an early-stage Merchant Insights AI agent that converts natural language questions into SQL using OpenAI models, with full CI/CD and observability.”
Tejasri Alla
Mid-level Full-Stack Python Developer specializing in banking microservices
“Built and led production LLM-agent systems in enterprise environments (Simmons Bank, Mindtree), automating support ticket triage end-to-end with strong reliability engineering (99.9% uptime, Prometheus/Grafana, ECS autoscaling, CI/CD rollback). Demonstrated clear business impact (55% faster handling, SLA compliance 72%→96%, 800+ hours saved/month, +18% CSAT) and mature eval/feedback loops that improved extraction accuracy by 21%.”
Farida Poor
Junior Machine Learning Engineer specializing in NLP and multimodal transformers
“Built and deployed LLM-powered agentic chatbot and text-to-SQL systems using LangGraph/LangChain (and Bedrock), structuring workflows as DAGs with planning/replanning and validation to improve tool-calling reliability and reduce hallucinations. Operates production feedback loops with online/offline metrics, drift detection, and LangSmith-based evaluation pipelines, and regularly partners with business stakeholders and clinicians using slide decks and visual charts.”
Snehitha Penumaka
Mid-level AI/ML Engineer specializing in predictive modeling and cloud ML pipelines
“LLM engineer/data engineer who has deployed production RAG systems for internal-document Q&A, building end-to-end ingestion, embedding, vector search, and FastAPI serving while actively reducing hallucinations and latency through rigorous retrieval tuning and caching. Also experienced in orchestrating cloud data pipelines (Airflow, AWS Glue, Azure Data Factory) and partnering with non-technical business teams to deliver AI solutions like automated document review.”
Pravalika Kuppireddy
Mid-level AI/ML Engineer specializing in Generative AI and intelligent automation
“LLM engineer who built and productionized a system to classify GitHub commits (performance vs non-performance) using zero-/few-shot approaches over commit messages and diffs, working at ~5M-record scale on multi-node NVIDIA GPUs. Experienced orchestrating end-to-end LLM pipelines with Airflow and GitHub Actions, and emphasizes reliability via testing, guardrails, and observability while collaborating closely with non-technical product stakeholders.”
RIYA CHADDHA
Mid-level Data Engineer and Business Analyst specializing in cloud ETL and analytics
“Data analyst with cross-industry experience spanning insurance analytics at L&T Infotech and experimental imaging analytics at Mylyser. Stands out for building scalable SQL/PySpark data pipelines, standardizing business-critical metrics like claims lifecycle and policy retention, and delivering measurable impact such as 50%+ faster query performance and a 15% reduction in claims settlement time.”
Samuel Charest
Mid-level Data Analyst specializing in marketing analytics and business intelligence
“Former Database Marketing Analyst with hands-on SQL experience supporting targeted casino marketing campaigns and a strong emphasis on validation accuracy. Also describes owning analytics work that standardized engagement metrics across teams, automated cohort/segmentation reporting, and helped identify onboarding friction that improved user engagement.”
David Alvarado
Junior Business Analyst specializing in data analytics and BI
“Analytics candidate with insurance domain experience at Chubb, combining strong SQL/Python data engineering for claims reporting with business-facing metric design in Power BI. Also built an MLB game outcome predictor that beat Vegas implied probabilities using public data, showing strong product thinking and applied modeling ability beyond standard BI work.”