Professional Summary
Senior ML Engineering Specialist with 6+ years of experience transforming complex business challenges into innovative AI solutions. Currently leading machine learning initiatives at Merck Sharp & Dohme LLC, with a proven track record of deploying production-ready ML systems across pharmaceutical manufacturing, public safety, and healthcare domains. Published researcher with 152 citations, combining deep technical expertise with practical business impact.
Download Resume
- Complete Resume (PDF) - Full professional resume
- LinkedIn Profile - Professional network
- GitHub Portfolio - Code repositories
- Google Scholar - Research publications
Core Competencies
🤖 Machine Learning & AI
Advanced Expertise: Deep Learning, Computer Vision, NLP, MLOps, Model Deployment Frameworks: TensorFlow, PyTorch, Scikit-learn, OpenCV, HuggingFace Specializations: Process optimization, Defect detection, Medical imaging, LLM fine-tuning
💻 Programming & Development
Languages: Python (Expert), C++, MATLAB, SQL, Shell Scripting Cloud Platforms: AWS, Google Cloud Platform, Azure Tools: Docker, Kubernetes, Git, JIRA, Bamboo Databases: MongoDB, PostgreSQL, MySQL
🔬 Research & Innovation
Publications: 7 peer-reviewed papers, 152 citations Domains: Healthcare AI, Computational Biology, Computer Vision Achievements: 97.86% accuracy in medical imaging, 95.99% in protein analysis
Professional Experience
🏢 Senior ML Engineering Specialist
Merck Sharp & Dohme LLC | June 2024 - Present
Greater Philadelphia, PA
Leading AI transformation in pharmaceutical manufacturing through cutting-edge ML solutions for process optimization, computer vision quality control, and predictive maintenance systems.
🔧 ML Engineer
SystemoneX | February 2024 - May 2024
Boston, MA
Rapid development and deployment of machine learning models for business applications, focusing on cloud-native solutions and algorithm optimization.
📊 Technology & Strategy Head
Northeastern University Sanskriti | January 2022 - December 2023
Boston, MA
Pioneered LLM applications with RAG implementation, achieving 50% operational efficiency improvement through data-driven strategies and A/B testing frameworks.
🛡️ Machine Learning Engineer
Motorola Solutions | May 2021 - January 2022
Somerville, MA
Developed production-ready computer vision systems for social distancing and face recognition, achieving 30% latency reduction and 30% improvement in accuracy.
🏥 Clinical Deep Learning Engineer
IIIT Hyderabad | December 2020
Hyderabad, India
Achieved 97.86% accuracy in medical imaging through weakly-supervised segmentation and explainable AI for mammogram analysis.
🧬 Machine Learning Engineer
IIIT New Delhi | December 2018 - December 2019
New Delhi, India
Developed protein-ligand interaction prediction models with 95.99% accuracy using advanced machine learning techniques.
Education & Certifications
🎓 Academic Background
- Master’s in Data Science - Focus on Machine Learning and AI
- Bachelor’s in Computer Science - Strong foundation in algorithms and systems
📜 Professional Development
- AWS Certified Solutions Architect (In Progress)
- Google Cloud ML Engineering (In Progress)
- Advanced Deep Learning Specialization
- MLOps and Production ML Systems
Technical Skills Matrix
| Category | Expert | Advanced | Intermediate |
|---|---|---|---|
| ML Frameworks | TensorFlow, PyTorch | Scikit-learn, OpenCV | Keras, XGBoost |
| Programming | Python | C++, SQL | MATLAB, R |
| Cloud | AWS | Google Cloud | Azure |
| DevOps | Docker, Kubernetes | Git, CI/CD | Terraform |
| Databases | MongoDB | PostgreSQL | MySQL, Redis |
Notable Achievements
🏆 Research Impact
- 152 citations across 7 published papers
- 97.86% accuracy in medical imaging AI
- 95.99% accuracy in protein-ligand prediction
- Open-source contributions to ML community
🚀 Production Systems
- 30% latency reduction in computer vision models
- 50% operational efficiency improvement through data strategy
- Multi-million dollar pharmaceutical process optimization
- Real-time AI systems deployed at scale
🌟 Leadership & Innovation
- Led cross-functional teams across multiple organizations
- Pioneered LLM applications before ChatGPT era
- Mentored junior engineers and researchers
- Published research in top-tier conferences
Contact Information
📧 Email: in.gaurav.mishra@gmail.com
📱 Phone: (857) 264-9813
📍 Location: Boston, MA (willing to relocate)
🔗 LinkedIn: linkedin.com/in/ingauravmishra
💻 GitHub: github.com/mishragauravgm
🎓 Google Scholar: Research Profile
Beyond the Resume
When not training neural networks, I’m training my voice in Hindustani Classical Music, fostering cats in the Boston area, and exploring New England’s hiking trails. I believe the best engineers are well-rounded individuals who find inspiration in diverse experiences - from the mathematical beauty of ragas to the patience learned from working with rescue cats.
“Innovation happens at the intersection of technical expertise and human curiosity.”
Source of data
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Using data with Python
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Start Python:
Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
import numpy as np
import pandas as pd
Open the file:
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat data.csv.
file_path = 'data.csv'
with open(file_path, 'r') as file:
Read data:
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lines = file.readlines()
Parse and process data:
Duis aute line_data irure dolor in reprehenderit in voluptate velit esse
cillum dolore eu fugiat nulla pariatur data.extend.
data = []
for line in lines:
line_data = line.strip().split(',') # Split the line into a list of values
line_data = [float(value) for value in line_data] # Convert values to floats
data.extend(line_data) # Extend the main list with values from the line
Compute summary statistics using NumPy:
Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum: data_array.
data_array = np.array(data) # Convert the list to a NumPy array
mean = np.mean(data_array)
median = np.median(data_array)
std_dev = np.std(data_array)
min_value = np.min(data_array)
max_value = np.max(data_array)
Display summary statistics:
Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod
tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,
quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo
consequat print.
print(f"Mean: {mean}")
print(f"Median: {median}")
print(f"Standard Deviation: {std_dev}")
print(f"Minimum Value: {min_value}")
print(f"Maximum Value: {max_value}")
Description of simulation parameters
| Parameter | Value | Language | Time period | Description |
|---|---|---|---|---|
| $\alpha$ | $1/2$ | French | 1930–1954 | Tempor dolor in |
| $\lambda$ | $e/2$ | French | 1930–1954 | Fugiat sint occaecat |
| $\gamma$ | $\ln(3)$ | Spanish | 1833–1954 | Duis officia deserunt |
| $\omega$ | $10^{-4}$ | Italian | 1930–1994 | Excepteur et dolore magna aliqua |
| $\sigma$ | $1.5$ | Portuguese | 1990–2023 | Lorem culpa qui |
| $\chi^2$ | $\pi^2$ | Portuguese | 1990–2023 | Labore et dolore |