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Vetted Machine Learning Engineers in New Jersey
Pre-screened and vetted in New Jersey.
Pranay Chamala
Mid-level Machine Learning Engineer specializing in MLOps, NLP, and LLM/RAG systems
R/isihka Bhumireddy
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and cloud ML on AWS
SAI BABU KARRE
Mid-level AI/ML Engineer specializing in GenAI, NLP, and AWS MLOps
Kalyani Kondepu
Mid-level Machine Learning Engineer specializing in healthcare and financial AI
Krishna K
Junior Machine Learning Engineer specializing in multimodal systems and LLMs
“Built and productionized a domain-specific LLM-powered RAG knowledge assistant at JerseyStem for answering questions over large internal document corpora, owning the full stack from FAISS retrieval and LoRA/QLoRA fine-tuning to AWS autoscaling GPU deployment. Drove measurable gains (28% accuracy lift, 25% latency reduction) and improved reliability through hybrid retrieval, grounded decoding, preference-model reranking, and Airflow-orchestrated pipelines (35% faster runtime), while partnering closely with non-technical stakeholders to define success metrics and ensure adoption.”
Kotesh Yelamati
Mid-level Machine Learning Engineer specializing in NLP, MLOps, and predictive risk modeling
Shriyansh Singh
Junior AI & Machine Learning Engineer specializing in LLM automation and RAG systems
Naveen Udata
Mid-level Machine Learning Engineer specializing in conversational AI and voice/LLM systems
Ganesh Chitlapally
Mid-level Machine Learning Engineer specializing in GenAI, RAG, and medical imaging
Chandrika Guntupalli
Mid-level Generative AI/ML Engineer specializing in LLMs, RAG, and MLOps
Tanmay Naik
Mid-level Machine Learning Engineer specializing in Generative AI and LLMOps
Vishesh Patel
Junior AI/ML Engineer specializing in Python ML, NLP, and model deployment
“Built and productionized a real-time social-media sentiment analysis system used by a marketing team to monitor brand/campaign performance. Experienced in orchestrating LLM workflows with LangChain (validation → prompting → parsing → post-processing), plus monitoring, retraining, and RAG-style retrieval using embeddings/vector stores to keep outputs reliable over time.”
Sujal Bais
Junior AI/ML Engineer specializing in applied machine learning and data pipelines
“Built and deployed an LLM-powered automation pipeline that ingests voice and documents, transcribes/extracts key information into structured data, and routes it through backend workflows using Python/FastAPI. Uses n8n to orchestrate multi-step AI processes with validation, retries, and monitoring, and iterates with stakeholders via rapid demos to refine changing requirements.”
Charitha sri Iddum
Mid-level AI/ML Engineer specializing in LLMs and RAG systems