Vinayak Chaturvedi

As a Sr. ML engineer at Syndigo LLC, I built robust AI and ML infrastructure from the ground up. My journey has
been marked by impactful achievements:
 Scaling Success: I’ve led large-scale ML projects within a tight-knit team of four, demonstrating ownership and
expertise. From inception to deployment, I’ve navigated the entire project lifecycle. We further expanded the team's strength to 25 consisting of multiple software engineers, data scientists and data engineers where I served as a leader to guide the team on best practices, security and processes.
 Problem Solver Extraordinaire: My toolkit includes data analysis, model development, ablation studies,
optimization, fine-tuning, RESTful API development and MLOps. I’ve turned complex challenges into elegant
solutions.
 Strategic Orchestrator: Collaborating with key stakeholders and leaders, I’ve orchestrated requirements gathering
sessions and brainstorming marathons. My strategic project planning ensures alignment with business goals.
 Cutting-Edge Research: The ML landscape is ever evolving, and I thrive on staying ahead. I’ve contributed to
applied research, bringing state-of-the-art solutions to the table—always mindful of practicality and relevance.
 Mentorship and Knowledge Sharing: Guiding junior team members is my passion. I’ve hosted year-long companywide ML training sessions, fostering a culture of continuous learning.
 LLM, NLP and Computer Vision Champion: I’ve led successful projects in Large Language Models (LLM), natural
language processing (NLP), speech synthesis and computer vision. Transforming data into insights is where I excel.
 Seamless Integration: Whether it’s securing models or integrating ML services into existing tech stacks, I ensure
smooth transitions.
Worked on wide variety of target tasks such as classification, summarization, question answering and generation
on textual, audio, image/video data. Conducted and led applied research on various ML techniques/models to solve
the underlying business problems generating significant financial revenue and immense end-user satisfaction.

My experience is resonant but not limited to:
1. Computer Vision:
o Feature-Based Methods:
i. Proficient in SIFT, SURF, and ORB—classic techniques for feature extraction and matching.
ii. Leveraged these methods for tasks such as object recognition, image stitching, and scene
reconstruction.
o Siamese Networks:
i. Designed and trained Siamese neural networks for similarity learning.
ii. Applications include face verification, product verification, and tracking objects across frames.
o CNN (Convolutional Neural Networks): Extensive experience with CNN architectures for:
i. Image classification (e.g., ResNet, VGG, Inception).
ii. Object detection (e.g., YOLO, Faster R-CNN).
iii. Semantic segmentation (e.g., U-Net, DeepLab).
o Large Language Models/ Diffusion:
i. Achieved high quality image in-painting and out-painting using diffusion models like stable
diffusion (SDXL).
ii. Built pipelines to integrate GPT-Vision into products.

2. Audio and Multi-Modal Models:
o Audio Deep Learning:
i. Investigated WaveNet, MelGAN, and Tacotron for speech synthesis.
ii. Applied CRNNs for music genre classification.
o Multi-Modal Fusion:
i. Explored late fusion (concatenation) and early fusion (parallel processing).
ii. Integrated vision and audio features for tasks like scene recognition within videos.
3. Natural Language Processing (NLP):
o RNNs (Recurrent Neural Networks):
i. Developed and fine-tuned LSTMs and GRUs for sequential data modeling.
ii. Applied them to tasks such as sentiment analysis, language modeling, and time series prediction.
o Entity Matching:
i. Devised algorithms for entity resolution across large text corpora.
ii. Utilized techniques like TF-IDF, Jaccard similarity, and Levenshtein distance.
o Textual Entailment:
i. Constructed models to determine logical entailment between sentences.
ii. Incorporated attention mechanisms and word embeddings for context understanding.
o BERT Models:
i. Fine-tuned pre-trained BERT models for specific NLP tasks.
ii. Achieved state-of-the-art results in sentiment analysis and question answering.
o LLMs (Large Language Models)/Transformers:
i. Explored transformer architectures (e.g., GPT, T5) for various NLP applications.
ii. Implemented self-attention and multi-head attention layers.
4. ML Techniques:
o Quantization and Pruning: Optimized neural networks during deployment by reducing weight precision
(quantization) using LORA and QLORA and removing unnecessary connections (pruning) using PEFT.
Achieved a delicate balance between accuracy and efficiency.
o Neural Architecture Search (NAS): Achieved a delicate balance between accuracy and efficiency using
Vertex AI (Google). This required a lot of computation resources and budget.
o Core Frameworks: Proficient in both PyTorch and TensorFlow frameworks. Designed custom layers,
implemented complex loss functions, and appreciated their unique features.
o Hardware Optimization: Explored parallelism, memory hierarchy, and energy efficiency trade-offs
amongst various CPUs and GPUs, deploying models on relevant hardware. LPU’s and NPU’s are popular
alternatives but our requirements did not have a necessity on that hardware.
o Hyperparameter Tuning: Fine-tuned models using grid search, random search, and Bayesian
optimization. Strived for optimal accuracy-efficiency trade-offs.
o Knowledge Distillation: Compressed large models while preserving performance. Explored techniques
like temperature scaling and attention transfer to achieve compressed student neural networks.
5. Production grade software systems for AI:
o End-to-End Pipelines:
i. Architected robust pipelines handling data prep, training, and deployment.
ii. Ensured seamless integration into micro-services with version control, monitoring, logging and
securing.
iii. Utilized Azure Kubernetes clusters to create the production host environment.
o RESTful APIs:
i. Designed RESTful APIs enabling CRUD operations using Python, FastAPI and flask.
ii. Generated requirements and guidance for software developers on MLOps and DevOps.

• Developed a MATLAB Application for National Road Research Alliance and Minnesota Department of Transportation with contract number : 1034192 (Research Grant).
• Link : https://dot.state.mn.us/mnroad/nrra/structure-teams/geotechnical/files/1034192-load-restriction-task-5-deliverable.pdf
• Designed, implemented and deployed an application (frontend and backend) based on MATLAB that predicts a safe passage on asphalt roads for heavy duty vehicles after a rainfall event.
• Successful presentation and demonstration to Minnesota Department of Transportation.
• Reviewed all documentation for accuracy, quality and compliance.
• Took corrective action to restore or maintain quality standards.

• Provided computer programming subject matter expertise to undergraduate students in C, C++, Java, Python and Javascript.
• Developed an application for user logging and scheduling at programming and assistance centre.

• Taught computer science undergraduate 400 level courses for over 30 students.
• Checked assignments, proctored tests, and provided grades according to university standards.
• Mentored students through office hours and one-on-one communication in assignments, labs and class projects.
• Coordinated homework and assignments for absent students.
• Worked with teachers in evaluating student progress, needs and gains.

• Planned lessons for allotted time to strengthen weak subjects and build programming skills.
• Collaborated with students to complete projects and assignments, identify lagging skills and correct weaknesses.
• Made lessons interesting and engaging using art and visual aids to bolster learning.
• Maintained detailed files and created charts tracking students learning progress.

• Supported classroom activities, including tutoring, grading homework and reviewing exams.
• Partnered with teacher to plan and implement lessons following school's curriculum, goals and objectives.

• Performed IT Audits for 30+ local and multi-national companies across multiple sectors like Banking and Capital Markets, Manufacturing and Oil and Gas.
• Performed Python code reviews and built VBA tools for automating jobs and improving project efficiency.
• Professional experience with enterprise resource management tools like SAP and Oracle ERP.
• Provided recommendations on board of directors and stakeholders on group's effectiveness, actions and future plans.

• Provided front-end website development using WordPress, Hubspot and other editing software.
• Planned website development, converting designs into usable web presence with SQL,HTML, PHP, JavaScript, CSS, AJAX and JSON coding.
• Employed search engine optimization tactics to increase reach of targeted audience.
• Oversaw technical issues and troubleshooting requests to resolve surfaced problems.

• Developed user interfaces with modern JavaScript frameworks, HTML and CSS.
• Designed and implemented web components across new and existing designs.
• Participated in project development through entire Software Development Lifecycle (SDLC).

• Received Graduate Assistant Scholarship.
• Received scholarship from National Science Foundation (NSF) for attending annual National Robotics Initiative (NRI) conference at Washington, D.C. (an invitation only meeting).