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Vetted NLTK Professionals
Pre-screened and vetted.
Senior Machine Learning Engineer specializing in LLMs, RAG, and multimodal AI
Mid-level Machine Learning Engineer specializing in LLM/VLM inference and RAG systems
Mid-level AI/ML Engineer specializing in cloud ML, NLP/LLMs, and real-time data pipelines
Senior Data Scientist specializing in GenAI, MLOps, and cloud data platforms
Mid-level AI/ML Engineer specializing in LLMs, RAG, and GenAI pipelines
Mid-level Software Test Engineer specializing in automation, API, and CI/CD
Mid-level Machine Learning Engineer specializing in NLP, time-series forecasting, and edge AI
Mid-level AI/ML Engineer specializing in NLP, LLMs, and fraud/AML analytics
Mid-level Machine Learning Engineer specializing in healthcare risk prediction and GenAI
Mid-level Machine Learning Engineer specializing in forecasting, NLP, and MLOps
Mid-level AI/ML Engineer specializing in NLP, fraud detection, and LLM applications
Mid-level Data Scientist specializing in ML, NLP, and scalable data pipelines
Mid-level Data Scientist specializing in NLP, risk analytics, and MLOps
Mid-level Data Scientist specializing in GenAI, NLP, and cloud MLOps
Junior Data & AI Analyst specializing in BI, LLM applications, and analytics
Bharath Reddy Nallu
Mid-level Machine Learning Engineer specializing in NLP and scalable MLOps
“Data/ML engineer in financial services (Northern Trust) who built a production RAG-based LLM system to connect structured transaction/portfolio data with unstructured market and internal documents for risk teams. Strong in end-to-end pipelines (AWS Glue/Airflow/PySpark), entity resolution, and taking models from prototype to reliable daily production with performance tuning (LoRA + TensorRT) and monitoring.”
Sai Manikanta Kasireddy
Mid-level Machine Learning Engineer specializing in cloud-native GenAI and RAG systems
“Built and productionized an internal GenAI chatbot that makes company policy/SOP knowledge instantly searchable, using a secure RAG architecture on AWS (Bedrock/Titan embeddings/OpenSearch Serverless, Textract/Lambda/S3 ingestion, Claude 3 Sonnet). Demonstrates strong MLOps/orchestration experience (Airflow, Step Functions with Lambda/Glue/SageMaker) and a rigorous reliability approach (RAGAS metrics, A/B testing, citation validation, monitoring), including collaboration with compliance stakeholders via review dashboards.”
Amit Prajapati
Junior Data Scientist / Software Engineer specializing in data pipelines and applied ML
“Built a production RAG chatbot for Worcester Polytechnic Institute that indexes 500+ webpages using FAISS + Llama 3, with strong grounding/hallucination controls (confidence thresholds and citations). Also has internship experience orchestrating multi-step ETL pipelines with AWS Step Functions and delivered a 30x faster fraud/claims triage workflow at Munich Re using association rules and stakeholder-friendly dashboards.”