AI/ML Model Engineer

As an AI/ML Model Engineer, you will play a crucial role in designing, developing, deploying, and maintaining scalable machine learning models and AI-powered applications. Your responsibilities will involve collaborating with data scientists, data engineers, and product teams to create impactful AI/ML solutions that address complex business problems. Key Responsibilities: - Design, develop, and implement various machine learning models (e.g., supervised, unsupervised, reinforcement learning, deep learning) to tackle challenging business issues. - Conduct extensive experimentation to evaluate model performance, refining them iteratively for optimal accuracy and efficiency. - Keep abreast of the latest advancements in AI/ML research and apply relevant techniques. Data Pipelining & Preparation: - Collaborate with data engineers to construct and maintain robust data pipelines for handling large datasets used for model training and inference. - Perform feature engineering to generate meaningful inputs for the machine learning models. Model Deployment & MLOps: - Develop strategies for deploying machine learning models into production environments, particularly on cloud platforms like AWS, Azure, and GCP. - Design and establish MLOps pipelines for continuous integration, continuous delivery, and continuous training of ML models. - Monitor deployed models for performance degradation, data drift, and model bias, and implement necessary retraining or recalibration strategies. System Integration: - Integrate AI/ML models and services into existing software systems and applications. - Create APIs and microservices for model inference. Collaboration & Communication: - Collaborate cross-functionally with data scientists to transition prototypes into production-ready solutions. - Work with software engineers to ensure seamless integration and scalability of AI/ML components. - Communicate complex technical concepts and findings to both technical and non-technical stakeholders. Problem Solving & Optimization: - Analyze and troubleshoot issues related to model performance, data quality, and system scalability. - Optimize models and infrastructure for efficiency, speed, and cost-effectiveness. Required Skills: - Strong proficiency in Python is essential, with experience in relevant libraries like TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy. - Familiarity with other languages such as Java, R, or C++ is a plus. - Experience with data structures, data modeling, SQL, and NoSQL databases. - Familiarity with big data technologies like Spark and Hadoop is advantageous. - Solid understanding of machine learning algorithms such as linear regression, logistic regression, decision trees, clustering, neural networks, and SVMs. - Experience with Large Language Models (LLMs), Generative AI, and Prompt Engineering. - Knowledge of specific domains like Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning. - Hands-on experience with at least one major cloud platform (AWS, Azure, GCP) and their AI/ML services. - Understanding of MLOps principles and tools for model deployment, monitoring, and versioning. - Experience with containerization technologies like Docker and Kubernetes. Please note that the job description contains additional details about the company, you can find more information at (ref:hirist.tech).,