3 Aiml Engineering Jobs

As a Machine Learning and Python Senior Engineer at JPMorgan Chase within the Asset and Wealth Management Team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives. You will be involved in the development and deployment of AI/ML solutions to drive business value. Your role includes designing, developing, and deploying state-of-the-art AI/ML/LLM/GenAI solutions that align with business objectives. You will conduct thorough evaluations of Gen AI models, iterate on model architectures, and implement improvements to enhance overall performance across applications. Additionally, you will develop appropriate testing frameworks for models and guide India team developers through the Machine Learning Development Lifecycle (MDLC). Collaboration is key in this role, as you will work directly with the US Core Client Service AI team to drive AI implementations from India. You are expected to collaborate with cross-functional teams to understand requirements and translate them into technical solutions. It is essential to stay updated with the latest trends and advancements in data science, machine learning, and related fields to continuously enhance your skills and knowledge. In terms of qualifications, capabilities, and skills, you are required to have formal training or certification in Computer Science, Engineering, or a related field with at least 3 years of applied experience. Proficiency in programming skills, particularly in Python, is necessary. Hands-on experience in applied AI/ML engineering, leading developers, and working in a fast-paced environment is crucial. You should also be proficient in programming languages like Python for model development, experimentation, and integration with Azure OpenAI API. Strong collaboration and communication skills are essential to work effectively with geographically spread cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects. Problem-solving and analytical skills with a keen attention to detail are highly valued. Experience with cloud platforms for deploying and scaling AI/ML models is advantageous. Preferred qualifications include experience in backend development, knowledge of SRE practices, and exposure to cloud automation technologies. Additionally, familiarity with large language models (LLMs), databases, programming languages, web frameworks, APIs, and microservices is beneficial. You should be able to assess and choose suitable LLM tools and models for various tasks, curate custom datasets, fine-tune LLM models, and design advanced LLM prompts. Your role may involve executing experiments to push the capability limits of LLM models and enhance their dependability.,

We are looking for a skilled and motivated Data Scientist with a strong background in backend development and AI/ML engineering. The ideal candidate should have experience in constructing, deploying, and expanding machine learning solutions using Python and modern ML frameworks. Responsibilities: - Develop and implement machine learning models, including advanced architectures like Large Language Models (LLMs). - Collaborate with various teams to create scalable AI-driven solutions using sound software engineering principles. - Utilize Python and object-oriented programming to build efficient, maintainable, and production-grade ML pipelines. - Work with popular ML frameworks such as TensorFlow, PyTorch, and Scikit-learn for model development and testing. - Integrate AI/ML models into applications via RESTful APIs while ensuring performance, scalability, and reliability. - Use tools like Hugging Face, LangChain, and OpenAI APIs for NLP and generative AI model development. - Manage containerized services using Docker for seamless deployment and reproducibility of ML solutions. - Follow best practices in version control (Git) and software development lifecycles to support collaborative and agile workflows. - Develop and maintain relational databases (e.g., MySQL, PostgreSQL, Oracle) to support data-driven applications. - Contribute to microservices-based architectures for AI/ML systems. - Apply agent communication protocols and multi-agent system design in relevant AI applications. - Analyze large datasets, extract actionable insights, and continuously improve models. Required Skills and Qualifications: - Minimum 4 years of experience in data science, machine learning, or backend development. - Proficiency in Python and solid foundations in object-oriented programming. - Hands-on experience with the entire machine learning model lifecycle, from training to production deployment and monitoring. - Understanding of scalable system design and microservices architecture. - Strong problem-solving skills, attention to detail, and knowledge of data structures and algorithms. - Excellent communication and collaboration abilities, with a knack for working in a dynamic team setting. Preferred Qualifications: - Expertise in Generative AI, particularly with Large Language Models (LLMs) like GPT and LLaMA. - Proficiency in prompt engineering, fine-tuning LLMs, and evaluating AI model performance. - Familiarity with AI system architecture, including orchestration frameworks and agent-based designs. - Experience with advanced AI tools, platforms, and APIs for scalable systems. - Strong communication skills and proven track record of effective collaboration in cross-disciplinary teams. - Knowledge of ethical AI principles, including model interpretability, fairness, responsible AI deployment, and ongoing monitoring.,

We are looking for a highly skilled AI/ML Engineer with expertise in developing machine learning solutions, utilizing graph databases like Neo4j, and constructing scalable production systems. As our ideal candidate, you should have a strong passion for applying artificial intelligence, machine learning, and data science techniques to solve real-world problems. You should also possess experience in dealing with complex rules, logic, and reasoning systems. Your responsibilities will include designing, developing, and deploying machine learning models and algorithms for production environments, ensuring their scalability and robustness. You will be expected to utilize graph databases, particularly Neo4j, to model, query, and analyze data relationships in large-scale connected data systems. Building and optimizing ML pipelines to ensure they are production-ready and capable of handling real-time data volumes will be a crucial aspect of your role. In addition, you will develop rule-based systems and collaborate with data scientists, software engineers, and product teams to integrate ML solutions into existing products and platforms. Implementing algorithms for entity resolution, recommendation engines, fraud detection, or other graph-related tasks using graph-based ML techniques will also be part of your responsibilities. You will work with large datasets, perform exploratory data analysis, feature engineering, and model evaluation. Post-deployment, you will monitor, test, and iterate on ML models to ensure continuous improvement in model performance and adaptability. Furthermore, you will participate in architectural decisions to ensure the efficient use of graph databases and ML models, while staying up-to-date with the latest advancements in AI/ML research, particularly in graph-based machine learning, reasoning systems, and logical AI. Requirements: - Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field. - 1+ years of experience in AI/ML engineering or a related field, with hands-on experience in building and deploying ML models in production environments and self-made projects using graph databases. - Proficiency in Neo4j or other graph databases, with a deep understanding of Cypher query language and graph theory concepts. - Strong programming skills in Python, Java, or Scala, along with experience using ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). - Experience with machine learning pipelines and tools like Airflow, Kubeflow, or MLflow for model tracking and deployment. - Hands-on experience with rule engines or logic programming systems (e.g., Drools, Prolog). - Experience with cloud platforms such as AWS, GCP, or Azure for ML deployments. - Familiarity with containerization and orchestration technologies like Docker and Kubernetes. - Experience working with large datasets, SQL/NoSQL databases, and handling data preprocessing at scale.,