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Vetted MLflow Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in LLMs, RAG, and GenAI pipelines
Mid-level Full-Stack GenAI/ML Engineer specializing in agentic AI and RAG systems
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 Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in risk modeling, NLP, and Generative AI
Mid-level Machine Learning Engineer specializing in Generative AI, NLP, and recommender systems
Mid-level Data Scientist specializing in ML, MLOps, and applied risk modeling
Mid-level Data Scientist specializing in GenAI, NLP, and cloud MLOps
Mid-level Machine Learning Engineer specializing in MLOps and LLM/RAG systems
Senior Data Engineer specializing in cloud lakehouse platforms for banking and healthcare
Senior AI/ML Engineer specializing in MLOps and Generative AI (LLMs/RAG)
Mid-level Applied AI Engineer specializing in Generative AI and RAG systems
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered 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.”