AI/ML Engineer

AI/ML Engineer

Location: NJ, NY, CT, PA, DE

We are seeking a skilled AI/ML Engineer to design, develop, deploy, and optimize machine learning and AI solutions at scale. The ideal candidate will have expertise in ML model development, deep learning frameworks, data engineering, and MLOps practices, along with strong problem-solving and software engineering skills.

Key Responsibilities

• Design, build, and implement machine learning and deep learning models for prediction, classification, NLP, computer vision, generative AI, and recommendation systems.

• Develop robust, scalable data pipelines for model training, evaluation, and deployment.

• Implement MLOps best practices including CI/CD, model versioning, monitoring, retraining, and automated deployment.

• Optimize models for performance, accuracy, and efficiency (model compression, quantization, GPU optimization).

• Collaborate with data engineers, product teams, and business stakeholders to define AI use cases and deliver ML solutions.

• Deploy models using cloud services (AWS, Azure, Google Cloud Platform) or on-premises environments with Docker/Kubernetes.

• Conduct research on new AI algorithms, LLMs, and emerging technologies; evaluate their applicability to business use cases.

• Develop APIs, microservices, and backend components to integrate AI models into production systems.

• Monitor data quality, data drift, and model performance in real-time environments.

Required Skills

• Strong programming experience in Python (NumPy, Pandas, Scikit-learn).

• Hands-on expertise with Deep Learning frameworks: TensorFlow, PyTorch, Keras.

• Experience with LLMs, transformers, and frameworks like Hugging Face, LangChain, or OpenAI APIs.

• Strong foundation in machine learning, deep learning, neural networks, mathematical/statistical concepts.

• Experience with data engineering tools: Spark, Databricks, Airflow, Kafka.

• Proficiency in SQL and handling large datasets.

• Experience with MLOps tools such as MLflow, Kubeflow, Sagemaker, Vertex AI, Azure ML Studio.

• Knowledge of cloud environments: AWS, Azure, or Google Cloud Platform.

• Experience building microservices using FastAPI, Flask, or Node.js.

• Version control and CI/CD with Git, Docker, Kubernetes, Jenkins or GitHub Actions.