Machine Learning Engineer

Coreintels•New York City, NY

9d•Hybrid

About The Position

Are you passionate about building and deploying intelligent systems that transform industries? At CoreIntels, we are pushing the boundaries of AI-driven automation, predictive analytics, and deep learning solutions. Join us to develop cutting-edge machine learning models and work alongside top-tier engineers in a dynamic, fast-paced environment. As a Machine Learning Engineer, you will design and deploy scalable ML solutions that power real-world applications across finance, healthcare, and enterprise AI systems. You will collaborate with data scientists, software engineers, and MLOps specialists to optimize machine learning workflows and deliver high-impact AI solutions.

Requirements

Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, Data Science, or a related field.
Strong experience with Python, PyTorch, TensorFlow, and Scikit-learn.
Expertise in ML model training, evaluation, feature engineering, and hyperparameter tuning.
Experience with cloud platforms (AWS, GCP, or Azure) for ML model deployment.
Familiarity with MLOps tools such as MLflow, Kubeflow, Airflow, and feature stores.
Proficiency in SQL & NoSQL databases for data management in AI applications.

Nice To Haves

Experience in LLMs, multimodal AI, or reinforcement learning.
Strong knowledge of distributed training techniques (Data Parallelism, Model Parallelism).
Familiarity with vector databases (Pinecone, FAISS) for retrieval-augmented generation (RAG).

Responsibilities

Develop and optimize machine learning models for natural language processing (NLP), computer vision, and recommendation systems.
Build scalable data pipelines to process large-scale datasets efficiently.
Deploy ML models into production using MLOps best practices (AWS SageMaker, GCP Vertex AI, Kubernetes, Docker).
Work with big data technologies like Apache Spark, Dask, Kafka, and TensorFlow Extended (TFX).
Optimize real-time inference pipelines for low-latency AI applications.
Collaborate with cross-functional teams to integrate machine learning models into production environments.
Continuously research and experiment with state-of-the-art (SOTA) AI techniques to improve model performance.