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Vetted Machine Learning Engineers
Pre-screened and vetted.
Mid-level Full-Stack GenAI/ML Engineer specializing in agentic AI and RAG systems
Senior Machine Learning Engineer specializing in GenAI and LLM-powered systems
Mid-level Machine Learning Engineer specializing in NLP, time-series forecasting, and edge AI
Mid-level Data Scientist / AI/ML Engineer specializing in financial services and GenAI
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 Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in NLP, fraud detection, and LLM applications
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 AI/ML Engineer specializing in LLM training, evaluation, and applied mathematics
Mid-level AI/Data Engineer specializing in LLMs, RAG pipelines, and cloud data platforms
Mid-level Machine Learning Engineer specializing in MLOps and LLM/RAG systems
Senior AI/ML Engineer specializing in MLOps and Generative AI (LLMs/RAG)
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.”
Sudheer koki
Mid-level AI/ML Engineer specializing in predictive modeling, data pipelines, and RAG systems
“Built and productionized an LLM-powered internal knowledge search system in a regulated environment, using embeddings/vector DB retrieval with strict grounding and confidence gating to reduce hallucinations. Reported ~45% accuracy improvement over keyword search and implemented end-to-end orchestration, monitoring, CI/CD, and incremental re-indexing to manage latency and data freshness while driving adoption with business stakeholders.”
Laxminarayana Yaga
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps
“Built and deployed a production RAG pipeline at PNC Financial Services to let risk/compliance analysts query millions of internal financial documents in natural language, reducing manual search and speeding regulatory validation. Demonstrates deep practical experience with large-scale document ingestion/OCR cleanup, retrieval performance tuning (hierarchical indexing, caching), and LLM reliability controls (grounding, citations, abstention), plus cloud orchestration on Azure and AWS.”
Nagasaikumar Jampani
Mid-level AI/ML Engineer specializing in Generative AI and RAG pipelines
“AI/LLM engineer with healthcare domain experience who built a production clinical support “chart bot” for Molina, including PHI-safe ingestion of 200k+ PDF policies, vector retrieval, and a fine-tuned LLaMA served via vLLM on ECS Fargate. Demonstrated measurable performance wins (HNSW + namespace partitioning; 30% inference latency reduction) and a rigorous evaluation/monitoring approach, while partnering closely with nurses and operations teams to shape workflows and guardrails.”