Computer Vision Ml Engineer
Resume:
Jay Ahmad Lead Al Developer/ML Engineer
***********@*****.***
Los Angeles, CA 90001
Profile
AI/ML Engineer with 10+ years of progressive experience designing, deploying, and scaling intelligent systems across NLP, computer vision, and predictive analytics. Adept in leveraging Python, Pytorch, Tensorflow, and cloud- native ML platforms to deliver production-grade Al solutions. Skilled in MLOps, model optimization, and data engineering pipelines for high-performance solutions. Proven track record of leading cross-functional teams, mentoring junior engineers, and driving measurable business impact through Al adoption. Experienced in both research-oriented innovation and real-world enterprise deployment. Recognized for bridging technical depth with strategic vision to accelerate Al transformation.
Skills
Machine Learning/Deep Learning
•Supervised, Unsupervised & Reinforcement Learning
•CNNs, RNNs, LSTMs, Transformers
•Generative Models (GANs, Diffusion Models)
•Transfer Learning & Few-Shot Learning
•Graph Neural Networks (GNNs)
•Time Series Forecasting & Survival Analysis
Programming & Data
•Python, R, SQL, Java, C++
•Pandas, NumPy, Spark, Hadoop, Dask
•PostgreSQL, MySQL, MongoDB, Cassandra
•GeoPandas for geospatial ML
MLOps & Deployment
•AWS SageMaker, Azure ML, GCP AI Platform
•MLflow, Weights & Biases, Kubeflow
•Flask, FastAPI, RESTful APIs, GraphQL, gRPC
•Docker, Kubernetes, CI/CD pipelines, Airflow
•Feature Stores (Feast, Tecton)
•Data Versioning: DVC, Delta Lake
Other Relevant Tools
•API Development: FastAPI, Flask, GraphQL
•Version Control: Git, GitHub, GitLab, Bitbucket
•Infrastructure as Code: Terraform, Ansible
•Monitoring & Observability: Prometheus, Grafana, ELK Stack
•Collaboration: JIRA, Confluence, Agile/Scrum
methodologies
•Technical Storytelling & Stakeholder Communication Al Specializations
•Computer Vision (OpenCV, YOLO, image segmentation)
•LLMs & Generative Al Applications
•LangChain, Llamalndex, Haystack
•Vector Databases: Pinecone, Weaviate, FAISS, Milvus
•Retrieval-Augmented Generation (RAG)
•Predictive Analytics, Recommender Systems
•Knowledge Graphs & Graph based ML
Visualization & Business Intelligence
•Matplotlib, Seaborn, Plotly, Tableau, Power BI, Looker
•Geospatial Visualization: Folium, Kepler
•Advanced Experimentation: A/B & Multivariate Testing
•Statistical Inference & Causal Analysis
Model Optimization & Explainability
•Model Compression: Quantization, Pruning, Distillation
•Interpretability: SHAP, LIME, Captum
•Bias & Fairness Auditing in Al models
•Adversarial Robustness Testing
•Differential Privacy in ML
•Responsible Al & Governance Frameworks
Professional Experience
Lead Al Developer, Blitz Mobile Apps 03/2021 – Present
•Designed and deployed enterprise-grade NLP chatbots using BERT and GPT models, significantly improving customer support response efficiency.
•Built and deployed real-time computer vision models with TensorFlow and OpenCV for advanced object detection and recognition tasks.
•Established robust MLOps pipelines with AWS SageMaker and GitHub Actions to streamline the full lifecycle of model training, testing, and deployment.
•Applied model optimization techniques such as quantization and pruning to enhance performance on edge devices and mobile platforms.
•Developed large-scale ETL data pipelines capable of handling millions of records daily, ensuring reliable data flow for ML training and analytics.
•Conducted applied research on federated learning and privacy-preserving Al applications in healthcare.
•Mentored and guided a team of ML engineers, helping them improve technical depth in Pytorch, deployment strategies, and scalable Al solutions.
•Partnered with cross-functional product teams to integrate Al-driven personalization features into mobile applications, strengthening user adoption and engagement. ML Engineer, Inno Zone 01/2019 – 03/2021
•Designed and implemented recommendation engines leveraging collaborative filtering, content-based, and hybrid approaches to enhance user personalization.
•Engineered large-scale data pipelines with Spark and Pandas to preprocess and manage multi-terabyte datasets for model training and experimentation.
•Built and deployed ML microservices with Flask and Docker to production, ensuring reliability and smooth integration with client platforms.
•Developed anomaly detection models to strengthen fraud detection systems, reducing risk in financial and e- commerce applications.
•Optimized model training by leveraging cloud resources, including cost-effective instance scheduling and hyperparameter tuning methods.
•Presented applied NLP research at several industry conferences, highlighting company expertise and building thought leadership.
•Collaborated closely with product and engineering teams to translate business requirements into scalable ML-driven features and services.
Junior Data Scientist, Appinnovativ Technologies 02/2015 – 12/2018
•Assisted senior data scientists in building predictive models for retail inventory management, helping improve supply chain efficiency.
•Supported the design and deployment of customer segmentation pipelines for e- commerce clients, enabling more targeted and effective marketing campaigns.
•Conducted analysis of user engagement metrics and prepared actionable insights that guided product teams in improving UI/UX design.
•Contributed to refining data pipelines for multiple client projects, ensuring better scalability and smoother processing.
•Built and maintained dashboards using Tableau and Matplotlib to present insights to business stakeholders in a clear and actionable manner.
•Performed A/B testing and statistical evaluation of marketing campaigns, generating insights that informed business growth strategies.
•Gained exposure to deep learning applications by assisting in pilot projects on computer vision and natural language processing.
Education
Bachelor's in Computer Science
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