2 Openais Api Jobs

As an AI Research Scientist, your role will involve developing the overarching technical vision for AI systems that cater to both current and future business needs. You will be responsible for architecting end-to-end AI applications, ensuring seamless integration with legacy systems, enterprise data platforms, and microservices. Collaborating closely with business analysts and domain experts, you will translate business objectives into technical requirements and AI-driven solutions. Working in partnership with product management, you will design agile project roadmaps that align technical strategy with market needs. Additionally, you will coordinate with data engineering teams to guarantee smooth data flows, quality, and governance across various data sources. Your responsibilities will also include leading the design of reference architectures, roadmaps, and best practices for AI applications. You will evaluate emerging technologies and methodologies, recommending innovations that can be integrated into the organizational strategy. Identifying and defining system components such as data ingestion pipelines, model training environments, CI/CD frameworks, and monitoring systems will be crucial aspects of your role. Leveraging containerization (Docker, Kubernetes) and cloud services, you will streamline the deployment and scaling of AI systems. Implementing robust versioning, rollback, and monitoring mechanisms to ensure system stability, reliability, and performance will also be part of your duties. Project management will be a key component of your role, overseeing the planning, execution, and delivery of AI and ML applications within budget and timeline constraints. You will be responsible for the entire lifecycle of AI application development, from conceptualization and design to development, testing, deployment, and post-production optimization. Enforcing security best practices throughout each phase of development, with a focus on data privacy, user security, and risk mitigation, will be essential. Furthermore, providing mentorship to engineering teams and fostering a culture of continuous learning will play a significant role in your responsibilities. In terms of mandatory technical and functional skills, you should possess a strong background in working with or developing agents using langgraph, autogen, and CrewAI. Proficiency in Python, along with robust knowledge of machine learning libraries such as TensorFlow, PyTorch, and Keras, is required. You should also have proven experience with cloud computing platforms (AWS, Azure, Google Cloud Platform) for building and deploying scalable AI solutions. Hands-on skills with containerization (Docker), orchestration frameworks (Kubernetes), and related DevOps tools like Jenkins and GitLab CI/CD are necessary. Experience using Infrastructure as Code (IaC) tools such as Terraform or CloudFormation to automate cloud deployments is essential. Additionally, proficiency in SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra) and expertise in designing distributed systems, RESTful APIs, GraphQL integrations, and microservices architecture are vital for this role. Knowledge of event-driven architectures and message brokers (e.g., RabbitMQ, Apache Kafka) is also required to support robust inter-system communications. Preferred technical and functional skills include experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) to ensure system reliability and operational performance. Familiarity with cutting-edge libraries such as Hugging Face Transformers, OpenAI's API integrations, and other domain-specific tools is advantageous. Experience in large-scale deployment of ML projects, along with a good understanding of DevOps/MLOps/LLM Ops and training and fine-tuning of Large Language Models (SLMs) like PALM2, GPT4, LLAMA, etc., is beneficial. Key behavioral attributes for this role include the ability to mentor junior developers, take ownership of project deliverables, contribute to risk mitigation, and understand business objectives and functions to support data needs. If you have a Bachelor's or Master's degree in Computer Science, certifications in cloud technologies (AWS, Azure, GCP), and TOGAF certification (good to have), along with 11 to 14 years of relevant work experience, this role might be the perfect fit for you.,

As an AI Azure Architect, your primary responsibility will be to develop the technical vision for AI systems that cater to the existing and future business requirements. This involves architecting end-to-end AI applications, ensuring seamless integration with legacy systems, enterprise data platforms, and microservices. Collaborating closely with business analysts and domain experts, you will translate business objectives into technical requirements and AI-driven solutions. Additionally, you will partner with product management to design agile project roadmaps aligning technical strategies with market needs. Coordinating with data engineering teams is essential to ensure smooth data flows, quality, and governance across different data sources. Your role will also involve leading the design of reference architectures, roadmaps, and best practices for AI applications. Evaluating emerging technologies and methodologies to recommend suitable innovations for integration into the organizational strategy is a crucial aspect of your responsibilities. You will be required to identify and define system components such as data ingestion pipelines, model training environments, CI/CD frameworks, and monitoring systems. Leveraging containerization (Docker, Kubernetes) and cloud services will streamline the deployment and scaling of AI systems. Implementation of robust versioning, rollback, and monitoring mechanisms to ensure system stability, reliability, and performance will be part of your duties. Moreover, you will oversee the planning, execution, and delivery of AI and ML applications, ensuring they are completed within budget and timeline constraints. Managing project goals, allocating resources, and mitigating risks will fall under your project management responsibilities. You will be responsible for overseeing the complete lifecycle of AI application developmentfrom conceptualization and design to development, testing, deployment, and post-production optimization. Emphasizing security best practices during each development phase, focusing on data privacy, user security, and risk mitigation, is crucial. In addition to technical skills, the ideal candidate for this role should possess key behavioral attributes such as the ability to mentor junior developers, take ownership of project deliverables, and contribute towards risk mitigation. Understanding business objectives and functions to support data needs is also essential. Mandatory technical skills for this position include a strong background in working with agents using langgraph, autogen, and CrewAI. Proficiency in Python, along with knowledge of machine learning libraries like TensorFlow, PyTorch, and Keras, is required. Experience with cloud computing platforms (AWS, Azure, Google Cloud Platform), containerization tools (Docker), orchestration frameworks (Kubernetes), and DevOps tools (Jenkins, GitLab CI/CD) is essential. Proficiency in SQL and NoSQL databases, designing distributed systems, RESTful APIs, GraphQL integrations, and event-driven architectures are also necessary. Preferred technical skills include experience with monitoring and logging tools, cutting-edge libraries like Hugging Face Transformers, and large-scale deployment of ML projects. Training and fine-tuning of Large Language Models (LLMs) is an added advantage. Educational qualifications for this role include a Bachelor's/Master's degree in Computer Science, along with certifications in Cloud technologies (AWS, Azure, GCP) and TOGAF certification. The ideal candidate should have 11 to 14 years of relevant work experience in this field.,