Job Title: Machine Learning Engineer Location: Berkeley Heights, NJ (Onsite – 5 days/week) Duration: 12 Months Contract Work Authorization: US Citizens or Green Card holders only Rate: $65–$70/hour (W2) Interview Process: 2 Rounds – Video Interviews Experience Required: 8+ Years LinkedIn Profile: Active profile required Job Overview: Fiserv is seeking an experienced Machine Learning Engineer to join our high-impact team in Berkeley Heights, NJ. This role is best suited for professionals passionate about building, optimizing, and deploying machine learning models at scale in a cloud-native AWS environment. You'll play a crucial role in delivering real-time and batch AI solutions that drive strategic business outcomes such as fraud prevention, customer analytics, and intelligent automation. Key Responsibilities: • Design, build, and deploy scalable machine learning models for real-world use cases like fraud detection, predictive analytics, and process automation. • Collaborate with cross-functional teams including data engineering, product, and DevOps to translate business goals into ML-driven solutions. • Perform data cleansing, feature engineering, and preparation for model development. • Implement and maintain end-to-end ML pipelines with robust MLOps practices including CI/CD, model versioning, and monitoring. • Deploy models using AWS ML services such as SageMaker, Glue, Lambda, and monitor for drift and performance degradation. • Integrate models into real-time systems with scalable inferencing frameworks. • Continuously evaluate new ML, MLOps, and cloud technologies to enhance platform capabilities. • Optionally contribute to innovation efforts around Generative AI, Retrieval-Augmented Generation (RAG), and agent-based AI workflows. Required Skills and Experience: • 6+ years of Python programming focused on data science and ML workflows. • Strong experience with Snowflake for data ingestion, transformation, and pipeline development. • Demonstrated expertise deploying models in AWS ML stack (SageMaker, Lambda, Glue, etc.). • Deep understanding of the full ML lifecycle: data ingestion → feature engineering → model training → deployment → monitoring. • Hands-on experience with real-time inferencing, model drift mitigation, and automated retraining pipelines. • Working knowledge of MLOps frameworks (e.g., MLflow, Airflow, Kubeflow) and container orchestration (Docker, Kubernetes). • Exposure to Generative AI, RAG, or agentic AI frameworks is a plus. • Familiarity with CI/CD for ML, automated testing, and model governance in production environments. • Excellent interpersonal, collaboration, and problem-solving skills.