Senior ML Engineer - Data Science & MLOps

Hassan Ahmad

Senior Machine Learning Engineer Data Scientist • Computer Vision • NLP • MLOps

Union City, NJ 973-***-**** ***********.****@*****.*** Summary

Senior Machine Learning Engineer with 9+ years building production AI systems across computer vision, NLP, LLM applications, and predictive modeling for regulated and high-scale environments including pharmaceutical manufacturing, retail, and enterprise SaaS. Track record of taking models from research prototype to deployed, monitored production systems serving dozens of facilities and thousands of retail locations. Experienced technical lead for small ML teams, with deep fluency across the modern LLM/GenAI and AI Agent stack, MLOps tooling, and multi-cloud deployment. Technical Expertise

AI/ML & GenAI: PyTorch, TensorFlow, Keras, JAX, Hugging Face Transformers, LangChain, LlamaIndex, ONNX, TensorRT, LLM Fine-Tuning (LoRA, QLoRA, PEFT), RAG, Vector Databases, Prompt Engineering, Model Distillation AI Agents & Virtual Agents: Agentic Workflows, Multi-Agent Orchestration, Tool-Use / Function Calling, LangGraph, AutoGen, Conversational AI, Virtual Assistants, Retrieval-Augmented Agents, Agent Memory & State Management Computer Vision: CNNs, ResNet, EfficientNet, ViT, YOLO, Faster R-CNN, U-Net, OCR, OpenCV, CLIP NLP & LLMs: BERT, RoBERTa, GPT-family Models, Named Entity Recognition, Text Classification, Summarization, Embeddings, Semantic Search

MLOps & Deployment: Docker, Kubernetes, MLflow, Weights & Biases, BentoML, Triton, KServe, CI/CD Pipelines, Model Monitoring, Drift Detection, A/B Testing

Cloud & Data Engineering: AWS (SageMaker, EC2, S3, Lambda), Azure ML, GCP Vertex AI, Apache Spark, Kafka, Airflow, SQL, PostgreSQL, Elasticsearch, Snowflake Professional Experience

Johnson & Johnson 03/2023 – Present

Senior Machine Learning Engineer

• Lead a team of 3 ML engineers, setting technical direction for model architecture decisions, running structured code reviews, and mentoring junior engineers through the full model lifecycle.

• Built predictive maintenance models for pharmaceutical manufacturing equipment using SQL-based data pipelines aligned with pharma data governance standards, enabling proactive intervention scheduling across 15+ facilities and shifting maintenance from reactive to predictive.

• Designed a ResNet-based computer vision quality control system for medical device inspection, reducing reliance on fully manual visual inspection and standardizing defect detection criteria across production lines.

• Built a retrieval-augmented generation (RAG) system using LangChain and a vector database to surface relevant manufacturing SOPs and compliance documentation for engineering teams, cutting document search time and supporting agentic workflows for routine compliance queries.

• Migrated model training and inference workloads to Azure ML alongside existing AWS infrastructure, enabling multi-cloud redundancy and giving manufacturing sites flexibility in compute sourcing.

• Established containerized MLOps pipelines with Docker and Kubernetes, compressing deployment cycles from weeks to days and enabling zero-downtime model rollouts. Summit Innovations 07/2020 – 01/2023

Machine Learning Engineer

• Owned ML models end-to-end from prototyping through production deployment as part of the core ML pipeline and deployment team.

• Built image understanding features using CNN-based transfer learning (ResNet, EfficientNet) to power content moderation and automated image tagging pipelines.

• Fine-tuned pre-trained transformer models (BERT, GPT-2) on domain-specific corpora, measurably improving performance across internal classification and generation benchmarks.

• Implemented distributed tracing, structured logging, and Grafana dashboards for ML services, significantly cutting mean time to resolution on model-serving incidents. Bed Bath & Beyond 07/2017 – 06/2020

Data Scientist

• Built a product recommendation engine using collaborative filtering and matrix factorization, directly contributing to improved sales conversion rates.

• Developed LSTM-based demand forecasting models to predict inventory needs across 1,000+ store locations, reducing overstock and stockout incidents.

• Designed customer segmentation models (K-Means, DBSCAN) that powered targeted marketing campaigns and personalized promotional offers.

• Built a pricing optimization system using XGBoost ensemble methods to inform dynamic pricing decisions based on demand elasticity signals.

• Designed and executed A/B tests to validate model performance pre-launch, improving recommendation clickthrough rate from 6% to 11% through iterative feature engineering. Cognizant Technology Solutions 08/2016 – 06/2017

Junior Data Scientist

• Built classification and regression models using Scikit-learn to support client forecasting and operational decisionmaking across multiple industries.

• Constructed scalable data preprocessing pipelines with Pandas and NumPy, cutting data preparation time by 30% for downstream modeling teams.

• Created Matplotlib and Seaborn visualizations translating model outputs into clear insights for non-technical stakeholders.

Certifications

• Google Professional Machine Learning Engineer – Google Cloud

• Databricks Certified Associate Developer – Apache Spark

• TensorFlow Developer Certificate – Google

• Deep Learning Specialization – DeepLearning.AI

Education

Bachelors of Science in Computer Science University of the Punjab, Pakistan

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