.png){kind=logo}
Vetted Machine Learning Engineers in Remote
Pre-screened and vetted in Remote.
Sharath Pampalker
Mid-level AI/ML Engineer specializing in real-time anomaly detection and AI agents
“Built a production real-time anomaly detection platform for high-frequency trading at HSBC, using a streaming stack (Pulsar + Spark Structured Streaming + AWS Lambda) and a transformer-based model combining time-series and numerical signals. Experienced in MLOps and safe deployment (Kubernetes, canary releases, MLflow/Grafana monitoring) and in aligning model performance with risk/compliance expectations through SLA-driven tuning and stakeholder-friendly dashboards.”
Kranthi Kumar Karupati
Mid-level Generative AI Engineer specializing in LLM apps, RAG, and MLOps
“LLM/GenAI engineer with US Bank experience building a production financial-document intelligence platform using LangChain/LangGraph, GPT-4, and Amazon OpenSearch. Delivered a RAG-based assistant for compliance/audit teams with grounded, cited answers, focusing on reducing hallucinations and latency, and deployed securely on AWS (SageMaker/EKS) with CI/CD and evaluation tooling (LangSmith, RAGAS).”
Ashwini Ramesh Kumar
Junior AI Software Engineer specializing in LLMs, RAG, and agent workflows
“Backend/ML-leaning engineer who built a content-based event recommender for FlowMingle using embeddings + HNSW vector search on Google Cloud, with Firebase as the backend and a managed recommendation lifecycle (15 recs/user, daily async generation, weekly deletion) now serving 1500+ users. Also led a cost-driven migration of ConvAI services to Azure AI using parallel request testing from a Unity client, with post-migration monitoring via logs and model evals; contributed to a Massachusetts law-enforcement conversation analysis system by expanding ingestion to PDF/TXT/Excel and multi-file inputs.”
Molli Dinesh
Mid-level AI/ML Engineer specializing in NLP, LLMs, and MLOps
“Built an AI-driven insurance policy summarization platform at Marsh, taking it end-to-end from messy PDF ingestion/OCR and custom extraction through LLM fine-tuning and AWS SageMaker deployment. Delivered measurable impact (25% reduction in manual review time, 99% uptime) and demonstrated strong production MLOps/LLMOps practices with Airflow/Step Functions orchestration, rigorous evaluation (ROUGE + human review), and continuous monitoring for drift, latency, and hallucinations.”
Shuvam Chatterjee
Mid-level AI/ML Engineer specializing in NLP, recommender systems, and Generative AI
Pratiksha Pawar
Mid-level AI/ML Engineer specializing in risk modeling, time-series forecasting, and MLOps
Likitha Rayapati
Mid-level AI/ML Engineer specializing in NLP, generative AI, and MLOps
Sekhar Chigurupati
Mid-level AI/ML Engineer specializing in MLOps, credit risk modeling, and graph ML
Vivek Rachakonda
Senior Machine Learning Engineer specializing in LLM systems and big data infrastructure
Vishehank Mishra
Mid-level Machine Learning Engineer specializing in NLP, RAG, and MLOps
Teja Chakilam
Mid-level Data Scientist / ML Engineer specializing in Generative AI and NLP
Akhila Kakumani
Mid-level AI/ML Engineer specializing in fraud detection and NLP in regulated industries
Vishnu Kodithala
Mid-level AI/ML Engineer specializing in fraud detection and real-time ML systems
Shravan Balamurugan
Junior Machine Learning Engineer specializing in LLMOps and computer vision for healthcare
Varshitha N
Mid-level AI/ML Engineer specializing in MLOps, Databricks Lakehouse, and GenAI RAG systems
Lokesh Miriyala
Mid-level AI/ML Engineer specializing in NLP and MLOps for regulated industries
Jahnavi Aella
Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and NLP
Sharanya Rao
Mid-level AI/ML Engineer specializing in NLP, LLMs, and RAG for finance and healthcare
“Built an AI lending assistant (RAG + DeBERTa) used by credit analysts to retrieve policies and past loan decisions, tackling real production issues like hallucinations, document quality, and sub-second latency. Deployed a modular, Dockerized AWS architecture (ECS/EMR + load balancer) with load testing, caching/precomputed embeddings, and CloudWatch monitoring, and used Airflow to automate scheduled data/embedding/vector DB refresh pipelines with retries and alerts.”
Lokesh Saipureddi
Mid-level Machine Learning/AI Engineer specializing in GenAI, RAG, and LLM inference
Kavya Vellanki
Mid-level AI/ML Engineer specializing in risk modeling, NLP, and Generative AI
Sri Lekkha Sakhamuri
Mid-level AI/ML Engineer specializing in generative AI and MLOps
Nagaraju Kanubuddi
Mid-level AI/ML Engineer specializing in fraud detection, recommender systems, and forecasting
“ML engineer/data scientist who built and deployed a real-time fraud detection platform at Citi on AWS SageMaker, processing 3M+ daily transactions and improving fraud response by 28%. Combines unsupervised anomaly detection (autoencoders) with ensemble models (XGBoost/Random Forest) plus Airflow/Step Functions orchestration, drift monitoring, and explainability (SHAP) to keep models reliable and compliant in production.”
Raj Patel
Junior Machine Learning Engineer specializing in LLMs and RAG systems
“Production-focused applied ML/LLM engineer who has deployed an LLM-powered RAG assistant and improved reliability through rigorous retrieval evaluation (recall/MRR), reranking, and guardrails that prevent confident wrong answers. Experienced running containerized ML/LLM services on Kubernetes (including AWS-managed layers) with CI/CD and observability, and has delivered a real-time predictive maintenance system using streaming sensor data and time-series anomaly detection in close partnership with maintenance teams.”