AI/ML Engineer | Software Engineer | Core Contributor to Amazon Nova LLM | Generative AI | Post-Training & Scalable ML Systems | 6+ Years in AI Research & Applied ML
I am an AI/ML software engineer with 6+ years of experience spanning applied machine learning, AI research, and large-scale model training. I began my career applying machine learning models to solve business problems, then spent two years in AI research advancing large language model capabilities.
Lead engineer supporting large-scale post-training for frontier LLMs and emerging multimodal models.
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Partnered with research teams to train early-stage multimodal models combining vision, language, and action-based inputs, enabling experiments in grounded reasoning and embodied interaction.
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Developed training pipelines that integrate visual encoders, cross-attention fusion layers, and instruction-tuned language heads, optimizing for sample efficiency and multi-sensor alignment.
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Led RLHF workflow development for multimodal systems, including preference data generation for vision-language tasks (image reasoning, spatial queries, action prediction).
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Developed curriculum-based sampling and multimodal reward-model training strategies that improved robustness on tasks involving perception, visual QA, and trajectory evaluation.
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Designed distributed replay buffers and sampling policies for long-horizon action sequences, improving convergence in tasks involving temporal dependencies and sequential decision-making.
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Supported parallelism (TP/PP/DP) for large-scale multimodal and LLM training runs, focusing on reliability, reproducibility, and stable multi-view data loading.
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Collaborated on model merging, evaluation pipelines, and safety-alignment experiments for multimodal policy models.
Collaborated with ML researchers to design, run, and analyze LLM training experiments, including supervised fine-tuning, instruction-tuning, and alignment evaluations.
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Built and maintained training pipelines for data preprocessing, tokenization, model configuration, hyperparameter search, validation checkpointing, and drift detection.
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Supported multi-round human-in-the-loop experiments: synthetic data generation, preference labeling workflows, reward-model retraining, and behavioral analysis of updated policies.
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Conducted rigorous analysis of model behavior in reasoning, multi-turn dialog, safety, and grounding tasks, producing reports that informed architecture and training-schedule changes.
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Contributed to early multimodal + embodied AI experiments, integrating LLMs with robotic simulators to study reasoning and control.
Developed an internal web application to manage document requests, integrated APIs for request completion and database interactions, passing all internal security and quality assurance tests necessary for launch readiness.
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Implemented a growing portfolio of agentic robotic processing automations in Python
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Developed an internal Python package to simplify and accelerate robotic process automation development
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Train and support a growing group of developers
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Implemented multiple machine learning classifiers using scikit-learn and NLTK to replace a manual intent classification process
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Lead automation efforts as the business process owner surpassing an organizational goal of $2,000,000 in expense reductions
Combined toolsets such as SQL Server, Oracle, and Tableau to deliver high-performance dashboards increasing the accessibility of data intelligence and analysis
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Analyzed prescribing habits leading to an increase in the utilization rate of a lifesaving drug by 19%, earning the organization an award for innovation
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Automated the updating of various tables in multiple SQL scripts using Python, contributing to an efficient and successful system upgrade
Collaborated across technical and non-technical teams eliciting requirements and developing solutions for the implementation of an enterprise data warehouse
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Led ongoing fraud detection analysis that had an initial impact of $38,000 in savings
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Mentored, and trained colleagues on tools, data flows, and data warehousing
