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Vetted ETL Pipelines Professionals
Pre-screened and vetted.
Junior Full-Stack Developer specializing in AI and cloud-native systems
Senior Full-Stack Engineer / Technical Lead specializing in cloud-native TypeScript and AI systems
Mid-level Machine Learning Engineer specializing in LLMs, Generative AI, and MLOps
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Generative AI Engineer specializing in LLM, RAG, and multimodal enterprise solutions
Mid-level Data Analyst specializing in BI, ETL, and cloud analytics
Mid-level Machine Learning Engineer specializing in healthcare and financial AI
Mid-level AI Engineer specializing in LLM agents and production ML systems
Sahil Gupta
Junior AI Software Engineer specializing in LLM agents, RAG, and healthcare NLP
“Backend engineer who built an agentic LLM system for private equity/finance that answers questions over enterprise contracts and documents using a vector-db RAG pipeline. Differentiator is a trust-focused citation framework (with highlighted source text) to reduce hallucinations in high-stakes workflows, plus strong DevOps experience deploying microservices on Kubernetes with Helm/GitOps and building Kafka real-time pipelines.”
Himanshu Kiran Garud
Mid-level Software Engineer specializing in full-stack systems and applied AI
“Backend/ML engineer who has built both enterprise data pipelines and real-time AI products: modular Python (Flask/FastAPI) services integrating automation scripts and low-latency ML inference (MediaPipe, PyTorch) plus OpenAI-powered feedback. Demonstrated measurable performance wins (~30% faster HR workflows; ~40% faster AWS pipelines across 100+ Oscar Health feeds) and strong multi-tenant/data-isolation patterns (schema-based isolation, RBAC, microservices).”
Rithiwik Garuda
Mid-level Full-Stack Engineer specializing in cloud-native, event-driven data platforms
“Backend/data engineer with hands-on production experience building Python (FastAPI/Flask) data enrichment services secured with Okta OAuth2 and monitored via Splunk/Dynatrace. Has delivered AWS event-driven and data-migration solutions (Lambda + Kafka to EKS; Glue from on-prem Oracle to S3/data lake) and modernized Informatica match/merge logic to cloud services using parallel-run parity validation and stakeholder sign-off.”
Tanweer Ashif
Mid-level AI/ML Engineer and Data Scientist specializing in LLMs and MLOps
“Data science/AI intern at University at Buffalo Business Services who built and deployed production systems spanning classic ML and LLM assistants. Delivered real-time competitor intelligence for a Cornell-partnered, $1B beverage launch by scraping/cleaning 5,000+ SKUs and deploying models via API, then built a domain-aware LLM assistant to modernize Excel-based workflows with strong grounding, privacy controls, and sub-5s latency.”
Michael St.Claire
Chief Architect specializing in microservices, cloud infrastructure, and enterprise AI agents
“Startup-focused engineering leader applying for a CTO role; previously served as founding CTO of a tech startup and held engineering/architecture roles across several others. Evaluates new ventures through market research and market demand, and emphasizes building from the ground up with strong teams (has not personally participated in fundraising).”
Pranathi Kamisetty
Intern AI Engineer specializing in LLMs, NLP, and conversational search
“Student building a production trip-planning LLM agent (LangChain + Streamlit) that routes user queries across multiple tools (maps/places/Wikipedia). Implemented zero-shot multi-label intent detection with priority rules to handle multi-intent requests, and collaborates with a startup product manager to shape tone, features, and user experience.”
Pallavi Yellisetty
Mid-level AI/ML Engineer specializing in predictive modeling, NLP, and recommender systems
“AI/ML manager who has deployed production NLP in healthcare—mining unstructured clinical notes and combining them with structured patient data to predict readmissions, with strong emphasis on data alignment and terminology normalization. Also experienced operationalizing ML with Airflow/MLflow and AWS Step Functions/SageMaker, plus stakeholder-facing Power BI dashboards (e.g., marketing customer segmentation).”
Abhishek Namdev Sawant
Mid-Level Backend Software Engineer specializing in Java microservices and cloud platforms
“Backend/platform engineer with payments and insurance domain experience (Cognizant), owning high-volume production systems end-to-end. Shipped a Spring Boot payment tokenization service with strong observability and phased migration that cut transaction latency ~30% and improved payment efficiency ~25%. Also productionized an ML-driven financial health/risk analytics pipeline with near real-time dashboards across 70+ schools, emphasizing interpretability, data quality, and drift monitoring.”
Manisha Miriyala
Mid-level Full-Stack Software Engineer specializing in Java/Spring, React, and AWS
“Backend/data engineer with production experience across event-driven Python ingestion services on AWS (EventBridge/SQS/MongoDB), serverless APIs (Lambda/API Gateway), and analytics ETL (Glue → Redshift). Has modernized legacy reporting into Node.js/React systems and demonstrated measurable SQL performance wins (minutes to seconds) plus strong incident ownership with validation, DLQs, and alerting.”
Bhanu Kiran
Mid-level Data Scientist & AI Engineer specializing in NLP, LLMs, and predictive analytics
“AI Engineer with production experience building an LLM-powered conversational scheduling assistant (rules-based + OpenAI GPT agents) and improving responsiveness by ~40% through architecture optimization. Strong in orchestration (Airflow), containerized deployments, and data quality (Great Expectations/PySpark), with prior work automating population health reporting pipelines (Azure Data Factory → Snowflake) and delivering insights via Tableau to non-technical stakeholders.”
Ayush Desai
Junior Full-Stack Software Engineer specializing in cloud-native web and VR systems
“Software developer with EdPlus experience building pragmatic internal tools (Next.js/TypeScript) and cloud automations (AWS) that feed Google Sheets and Looker Studio dashboards for operational visibility across 30+ student workers. Also worked on an IEEE Xplore-published missing-person tracker using a SPARQL graph database on Azure with a TypeScript/React frontend, and has experience navigating legacy access constraints (OpenVPN) while enabling remote VR previews via ngrok.”
Deva Sai Kumar Bheesetti
Mid-level Full-Stack Engineer specializing in data automation, cloud & AI
“JavaScript engineer who effectively "maintains" an internal open-source-style React/Node.js shared library used by multiple teams—owning API stability, semantic versioning, CI/testing, logging, and documentation. Demonstrates strong cross-team debugging and change-management skills (schema-driven refactors, feature flags, validation layers) to ship new features without breaking existing workflows, plus a profiling/benchmarking-driven approach to performance.”
Tanisha Pradeep Bhola
Mid-level Software Development Engineer specializing in backend, cloud, and microservices
“Accenture engineer with hands-on experience taking an NLP sentiment analysis system from prototype to production, emphasizing robustness to noisy data, scalability, and observability (dashboards for latency/error/throughput). Also supports customer-facing teams with demos and PoCs, translating client requirements into secure, scalable architectures and troubleshooting LLM/agent workflows via logs and step-level traces.”
Yang Qi
Mid-level Full-Stack Software Engineer specializing in AI platforms and data visualization
“Full-stack engineer with healthcare/bioinformatics experience who built a real-time genomic data analysis and 2D visualization feature (React/TypeScript + D3, FastAPI) at University of Utah Health, deploying on AWS ECS Fargate with monitoring and measuring engagement via Google Analytics. Also built AWS Lambda-based ETL pipelines for lab data ingestion using pandas/NumPy with reliability patterns (idempotency, retries, CloudWatch alerting) and drove maintainability improvements through shared component libraries and React hooks.”