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Vetted FAISS Professionals
Pre-screened and vetted.
Mid-level Data Scientist specializing in Generative AI, RAG systems, and MLOps
Mid-level AI/ML Data Engineer specializing in secure ML pipelines and AI governance
Mid-level Full-Stack Software Engineer specializing in cloud microservices and GenAI
Senior Data Scientist / AI-ML Engineer specializing in LLMs, NLP, and MLOps
Mid-level GenAI/ML Engineer specializing in RAG, semantic search, and LLM systems
Mid-level Data Engineer specializing in FinTech and AI-ready data platforms
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 Full-Stack Software Engineer specializing in cloud-native and AI-powered systems
Senior Full-Stack Software Engineer specializing in Python, React, and LLM-powered applications
Sai Krishna Sriram
Mid-level Generative AI & ML Engineer specializing in production LLM and RAG systems
“AI/ML engineer who shipped a production blood-test report understanding and personalized supplement recommendation product, using a LangGraph multi-agent pipeline on AWS serverless with OCR via Bedrock and RAG over vetted clinical research. Also built end-to-end recommender system pipelines at ASANTe using Airflow (ingestion, embeddings/features, training, registry, batch scoring/monitoring) with KPI reporting to Tableau, with a strong focus on safety, evaluation, and measurable reliability.”
Dhruv Kamalesh Kumar
Mid-level Generative AI Engineer specializing in LLM agents and RAG applications
“GenAI builder and technical lead with ~2 years of hands-on production experience, including GENIE (a GenAI sandbox for ~44,000 Massachusetts public-sector employees) and A-IEP, a multilingual platform helping parents understand complex IEP documents (cut processing from ~15 minutes to ~2 and used by 1,000+ parents). Strong in RAG/agentic architectures, AWS serverless + Step Functions orchestration, and rigorous evaluation/guardrails for reliable real-world deployments.”
YESHA BHAVSAR
Intern Software Engineer specializing in full-stack and AI applications
“Built and deployed an "AI Closet" application as a personal product, owning full-stack and applied AI features end to end. Particularly interesting for recruiters because they combined Next.js/TypeScript product engineering with practical AI systems work, including OpenAI vision-based smart form fill, personalized recommendation learning, and a grounded RAG application with evaluation and regression testing.”
Nikhil Tatikonda
Intern AI/ML Engineer specializing in LLM agents, RAG, and automation workflows
“AI automation builder who shipped an OpenAI-powered weekly "trending AI tools" WoW reporting system (65 categories) that reduced a 6–7 hour manual process to ~10 minutes at negligible API cost. Also building a RAG-based content creation prompt engine that turns PDFs into storyboards with fact-checking/traceback to source lines, plus experience with AWS deployment components (Lambda, ECR, App Runner, Bedrock, API Gateway) and GitHub Actions.”
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.”
YESWANTH REDDY CHEREDDY
Mid-level ML Engineer specializing in LLMs, Generative AI, and MLOps
“AI/ML engineer with production experience building an enterprise network-fault prediction assistant that combines anomaly detection (Isolation Forest + LSTM) with an LLM layer for incident diagnosis and recommended resolutions. Hands-on with orchestration (Airflow, Prefect, Dagster) to run ETL/ELT and automated training/fine-tuning workflows, and has delivered AI solutions with non-technical stakeholders (retail customer support ticket categorization/response suggestions).”
Anjana Priya Swathi Samudrala
Junior Full-Stack AI Developer specializing in LLMs and RAG applications
“Product-minded software engineer who owned a Shopify POS app end-to-end at Swym, shipping an MVP and then scaling iteration speed with E2E automation and CI/CD—resulting in a Shopify Badge, Top-5 App Store ranking, and +40% new user acquisition. Also built an ESG insights tool using React/TypeScript + FastAPI with Snowflake and a RAG pipeline, plus microservices patterns (async jobs, queues, DLQs, autoscaling) and internal Metabase/SQL analytics dashboards.”
Isha Harne
Intern Software Engineer specializing in ML applications and LLM platform engineering
“Full-stack engineer who builds and scales customer-facing and internal AI products end-to-end (React/TypeScript/FastAPI/MongoDB) with strong product instrumentation and rapid MVP iteration. Built an AI-powered code review assistant adopted across teams and integrated into CI/CD, reducing manual review time by 30%+, and has hands-on experience with LLM retrieval/reasoning systems (LangChain + FAISS) and microservices scaling using RabbitMQ, Docker, and AWS.”
Monthir Ali
Senior AI/ML Engineer specializing in LLMs, RAG, and VR/XR multimodal systems
“PhD researcher (University of Utah) who built a production RAG-powered Virtual Reality Research Assistant to answer lab research questions with concrete citations. Implemented an end-to-end LangChain pipeline using PyPDFLoader, chunking strategies, OpenAI embeddings, and ChromaDB, with emphasis on grounding to reduce hallucinations and ensure research-grade accuracy. Collaborated closely with a non-technical PhD advisor to scope requirements, manage cost constraints, and demo iterative progress.”