Top MLOps Engineers

MLOps Engineer specializing in designing scalable, production-grade AIecosystems. Expert in bridging the gap between business OKRs and technical executionthrough robust CI/CD, automated workflows, and advanced GenAI architectures. Proven trackrecord in deploying high-integrity systems involving RAG, Knowledge Graphs, and autonomousAI agents within highly regulated environments like HealthTech and IoT.

Passionate engineer with the ability to apply ML techniques & algorithms to solve real-world business problems. Targeting the technology-oriented, top organizations with challenging assignments at the MLOps position, possess an excellent track record of delivering AI models, In a cost effective and robust manner. * Knowledge of AI platforms such as Vertex AI and AWS Sagemaker. * Skilled in libraries such as Keras, TensorFlow, Sklearn, NumPy, and Pandas. * Deep Learning Techniques include ANN, CNN, and NLP.

Passionate MLOps Engineer with expertise in deploying, scaling, and managing machine learning models in production environments. Leveraging strong skills in CI/CD, cloud platforms, and infrastructure automation to bridge the gap between data science and operational excellence.

DevOps/MLOPS professional with 7+ years of experience in building scalable infrastructure, automating CI/CD pipelines, and ensuring high system reliability. Skilled in Kubernetes, Terraform, Jenkins, and cloud platforms (AWS, GCP, Azure). Experienced in MLOps practices like model deployment, monitoring, and experiment tracking using MLflow/Kubeflow. Strong focus on reliability, automation, and cost optimization across multi-cloud environments."

I’m a Machine Learning Operations Engineer at BetterUp, where I focus on designing and deploying large-scale ML systems and tools that bring machine learning into real-world applications. My professional journey began as a Backend Software Developer (2015–2018), during which I gained expertise in designing and developing software for high-performance mobile solutions. My strengths include RESTful web services, object-oriented programming, and data modeling, complemented by strong troubleshooting, documentation, and clean coding practices. After earning my Master’s in Data Science from Northeastern University, Boston, in 2020, I joined Mindbody as a Machine Learning Engineer. Over three years (2019–2022), spanning internships and full-time roles, I transitioned into the MLOps domain, where I contributed to the development, operationalization, and delivery of ML systems, including recommender systems. In addition to my technical skills, I am a certified AWS Associate Solutions Architect (2022), further supporting my ability to build scalable, cloud-based solutions.

As a BSc graduate in Artificial Intelligence & Machine Learning, I am passionate about turning data and machine learning models into scalable, production-ready solutions. My focus is on Machine Learning Operations (MLOps)—building robust pipelines, automating workflows, and deploying AI models efficiently on cloud platforms like AWS, Azure, and GCP. I have hands-on experience with Python, TensorFlow, PyTorch, Keras, and Scikit-learn, along with MLOps tools such as MLflow, Kubeflow, and Airflow. I enjoy solving challenges at the intersection of AI, software engineering, and DevOps, whether it’s optimizing model performance, implementing CI/CD pipelines, or ensuring smooth end-to-end deployment.

Experienced DevOps/MLOps Engineer with 6+ years of expertise in optimizing software development and deployment processes. Proficient in CI/CD pipelines, automation, and cloud platforms (AWS, Azure), with a strong track record of streamlining workflows and enhancing project efficiency. Currently focused on setting up end-to-end MLOps pipelines to deploy machine learning models with AWS SageMaker. Additionally, adept at working with large language models (LLM) and proficient in machine learning model deployment practices.

Skilled DevOps Engineer with 7 years of hands-on experience supporting, automating, and optimizing mission critical deployments in AWS, leveraging configuration management, CI/CD, and DevOps processes.