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Vetted Classification Professionals
Pre-screened and vetted.
Kunal Mahato
Mid-level Software Engineer specializing in AI, full-stack systems, and platform engineering
“Full-stack/AI engineer with experience spanning supply-chain product deployments, biomedical agentic search, and research-grade RAG evaluation. Stands out for owning customer-facing migrations at scale (including 216,000 historical shipments), building measurable LLM systems, and pairing AI experimentation with rigorous evals, rollout controls, and auditability.”
“Backend engineer focused on productionizing LLM systems: built a FastAPI-based RAG and multi-agent automation platform deployed with Docker/Kubernetes, prioritizing safe execution and reduced hallucinations. Experienced in refactoring monolithic ML services with feature-flagged incremental rollouts, and implementing JWT/RBAC plus row-level security (e.g., Supabase) for secure, scalable APIs.”
Tejal Mane
Mid-level Machine Learning Engineer specializing in GenAI, LLMs, and real-time ML systems
“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.”
Varsha Hemakumar
Mid-level ML/AI Engineer specializing in NLP, RAG pipelines, and financial risk & fraud systems
“Built and shipped LLM/RAG systems in finance and startup settings, including a Goldman Sachs document intelligence platform that indexed ~8TB of regulatory filings and delivered cited, conversational answers with <2s latency—cutting compliance research by ~4.5 hours per batch. Also developed LangChain-based agent workflows at Finta to automate CRM enrichment and investor lookup with strong testing, tracing (LangSmith), privacy guardrails, and auditability.”
Tejaswi Rao
Mid-level Machine Learning Engineer specializing in MLOps and GenAI analytics
“ML/LLM practitioner who has deployed a production RAG-based trouble-call identifier using multiple datasets (device, network, past complaints). Experienced in end-to-end MLOps (FastAPI + Docker + Kubernetes with HPA) and in evaluating/monitoring LLM behavior to reduce hallucinations, with additional applied work in forecasting/anomaly detection and churn prediction for retention campaigns.”
SASIREKHA GULIPALLI
Mid-level Data Analyst specializing in procurement, supply chain analytics, and applied machine learning
“Strategic sourcing professional specializing in seasonal apparel supply chains, combining Coupa/JD Edwards analytics with Excel/Python modeling and Power BI dashboards to drive cost reduction and OTIF gains. Notable for rapid mitigation of a 10-day factory delay affecting 12 holiday SKUs (preserved 95% of revenue) and for automating PO workflows to cut cycle time by 4.2 days and improve OTIF by 15%.”
Jiajun Long
Junior Robotics Researcher specializing in vision-based manipulation and learning-based control
“Robotics software candidate with experience spanning simulation (MuJoCo, Gazebo, Webots) and ROS1/ROS2 development, including hardware-oriented work on a hexapod and a Mecademic Meca500 R3 arm. Built a visually guided interactive indoor robot system using a CV pipeline plus POMDP + imitation learning with PPO-based residual RL, and has practical debugging experience improving LiDAR SLAM stability and migrating sensor interfaces from ROS1 to ROS2.”
Keerthana Priya
Mid-level Data Analytics & ML Engineer specializing in NLP, LLMs, and cloud data platforms
“At KPMG, built and productionized a secure RAG-based LLM assistant that lets business and risk stakeholders query data warehouses in natural language, reducing dependence on data engineers for ad-hoc analysis. Demonstrates strong production rigor (Airflow orchestration, CI/CD, containerization), retrieval/embedding tuning (rechunking, semantic abstraction for structured data), and reliability controls (confidence thresholds, refusal behavior, monitoring and canary evals).”
Ralish Routray
Mid-level Data Scientist & Machine Learning Engineer specializing in fraud and forecasting
“ML/LLM practitioner who has shipped production RAG systems (summarization + Q&A) and end-to-end Airflow-orchestrated demand forecasting pipelines at NEON IT. Strong focus on reliability—uses evaluation scripts, retrieval/chunking tuning, validation/retries/alerts, and stakeholder-driven iteration to make AI workflows consistent and usable.”
Sowmya Sree
Mid-level Machine Learning Engineer specializing in LLM agents, RAG, and MLOps
“Built production LLM systems including a real-time customer feedback analysis and workflow automation platform using RAG and multi-agent orchestration with confidence-based human escalation, addressing privacy and legacy integration challenges. Also automated ML operations with Airflow/Kubernetes (e.g., daily churn model retraining) cutting retraining time to under 30 minutes, and demonstrates a rigorous testing/monitoring approach plus strong non-technical stakeholder collaboration.”
Hamidreza Lotfalizadeh
Mid-level AI/ML Engineer specializing in LLM agents, RAG, and ML systems
“At Inertia Systems, built a production LLM-powered ingestion pipeline that converts heterogeneous sources (PDF/JSON/IFC/SQL and financial tables) into standardized text and uses GraphRAG to construct a knowledge graph with verified dependency relationships. Also has hands-on HPC orchestration experience with SLURM, including creating a custom wrapper process manager to improve resource utilization under restrictive scheduling policies.”
Jaskirat Singh
Mid-level Software Engineer specializing in cloud infrastructure and data platforms
“Infrastructure/data platform engineer with hands-on GCP production experience, especially Bigtable, who led a migration from Azure Cosmos DB Cassandra API to Bigtable that removed throttling and cut costs by 50%+. Stands out for combining distributed data architecture, zero-downtime Kafka migration strategy, and Terraform/Python automation for deterministic multi-region GKE operations.”
Kishan Peesapati
Senior AI Engineer specializing in Generative AI and RAG applications
“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.”
Narayanaroyal Marisetty
Mid-level Data Scientist/ML Engineer specializing in healthcare AI and MLOps
“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.”
Rakesh Kolagani
Mid-level AI/ML Engineer specializing in MLOps and LLM-powered applications
“AI/ML engineer with production experience building a RAG-based internal analytics assistant (Databricks + ADF ingestion, Pinecone vector store, LangChain orchestration) deployed via Docker on AWS SageMaker with CI/CD and MLflow. Strong focus on real-world constraints—latency/cost optimization (LoRA ~60% compute reduction), hallucination control with citation grounding, and enterprise security/governance. Previously at Intuit, delivered an interpretable churn prediction system (PySpark/Databricks, Airflow/Azure ML) that improved retention targeting ~12%.”
Pooja Murigappa
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and MLOps in Financial Services
“ML/LLM engineer at Charles Schwab who built a production loan-advisor chatbot integrated with internal knowledge and loan-calculator APIs, adding strict numeric validation to prevent rate hallucinations and optimizing context to control costs. Also runs ~40 Airflow DAGs orchestrating retraining/ETL/drift monitoring with an automated Snowflake→SageMaker→auto-deploy pipeline, and uses rigorous testing plus canary rollouts tied to business metrics and compliance constraints.”
Ruijing Wang
Intern Data Scientist specializing in healthcare AI and experimentation
“Human-AI Design Lab practitioner who productionized a wearable-health anomaly detection system by evolving a standalone autoencoder into a hybrid autoencoder + GPT-based approach, backed by PySpark ETL and MLOps on AWS SageMaker/MLflow. Also has applied LLM troubleshooting experience (fine-tuned FLAN-T5 summarization) and partnered with BI teams to run A/B tests and improve retention via feature stores and experimentation.”
Nivedita Shainaj Nair
Mid-level ML Data Engineer specializing in MLOps and scalable healthcare data pipelines
“Data/ML platform engineer with healthcare (Cigna) experience owning an end-to-end pipeline spanning Airflow + Debezium CDC ingestion, PySpark/SQL transformations, rigorous data quality gates, and feature-store/API serving for ML training and inference. Worked at 10+ TB scale and cites a ~30% latency reduction plus stronger reliability via idempotent design, monitoring, and backfill-safe reprocessing; also built pragmatic early-stage data pipelines at Frankenbuild Ventures.”
Shweta Gupta
Senior Backend Software Engineer specializing in Java microservices, Kafka, and AWS
“AI engineer who shipped a production chat assistant for a storage company by building the underlying RAG-style knowledge base (document ingestion, chunking/embeddings, FAISS vector store) and an admin update interface to keep content current. Also has full-stack delivery experience (Python REST APIs + React/TypeScript UI) and AWS operations using Terraform/Jenkins, including handling a real production performance incident by optimizing DB queries and adding auto-scaling.”
Arbaaz Meghani
Senior Machine Learning Engineer specializing in conversational AI and healthcare ML
“ML/AI engineer focused on taking LLM products from experiment to production, with hands-on ownership of a RAG-based customer support system that improved response quality by 35% and cut latency by 30%. Stands out for combining product impact with production rigor across retrieval tuning, safety guardrails, monitoring, and reusable Python/FastAPI services that accelerated adoption across teams.”
Venkata Surendra Kommineni
Senior AI/ML Engineer specializing in Generative AI and agentic systems
“Built and deployed an agentic RAG assistant in production to automate enterprise knowledge search and multi-step workflows with tool calling, tackling real-world issues like hallucinations, retrieval accuracy, and latency. Demonstrates strong LLMOps and orchestration depth (MLflow, Airflow, LangGraph/LangChain/LlamaIndex) plus a metrics-driven approach to agent testing/evaluation and cross-functional delivery with business stakeholders.”
Jareena kowsar shaik
Mid-level Machine Learning & GenAI Engineer specializing in LLMs, RAG, and NLP
“Built and deployed an LLM-powered customer support assistant (“Notable Assistant”) focused on automating common post-customer queries while maintaining multi-turn context and meeting scalability/latency needs. Experienced with production orchestration and operations using Kubernetes and Apache Airflow (DAG-based ETL, scheduling, monitoring/alerts), and has partnered closely with customer service stakeholders to align chatbot behavior with brand voice through iterative testing.”
Bharath TVS
Senior Data Scientist specializing in NLP, LLMs, and Computer Vision
“Applied NLP/ML engineer with experience at KeyBank and Novartis building production document intelligence and entity-resolution systems in finance and healthcare. Has delivered end-to-end pipelines (Airflow + AWS) using transformers (DistilBERT/Sentence-BERT), vector search (FAISS/Milvus/Pinecone), and human-in-the-loop labeling to achieve measurable gains (40%+ faster queries; up to 88% F1 and 93% precision/90% recall in entity linking).”
Janvitha Mandyam
Mid-level AI/ML Engineer specializing in Generative AI and NLP
“GenAI/LLMOps practitioner who deployed a production RAG-based customer service and knowledge retrieval system for a global bank using LangChain, FAISS/Azure Cognitive Search, GPT-4/Claude, and Guardrails—driving a reported 35% Q&A accuracy lift while reducing handle time and escalations. Also partnered with non-technical leaders at CVS Health to deliver ML-driven supply chain risk and inventory insights via anomaly detection, NLG summaries, and stakeholder-friendly dashboards.”