Description
- 7+ years of experience in Data Science with a background in machine learning, deep learning, and natural language processing.
- Robust background in traditional AI methodologies, encompassing both machine learning and deep learning frameworks.
- Familiarity with model serving platforms such as TGIS and vLLM.
- Hands-on experience in transformer-based and diffuser-based models (e.g., BERT, GPT, T5, Llama, Stable diffusion) is desirable. Experience in testing AI algorithms and models is advantageous.
- Proficiency in Python, C++, Go, Java, and relevant ML libraries (e.g., TensorFlow, PyTorch) to develop production-grade quality products is essential.
- Proficient in full-stack development, adept at frontend (HTML, CSS, JavaScript) and backend (Django, Flask, Spring Boot). Experience integrating AI tech into full-stack projects is a plus. Skilled in integrating, cleansing, and shaping data, with expertise in various databases including open-source databases like MongoDB, CouchDB, CockroachDB.
- Proficient in developing optimal data pipeline architectures for AI applications, ensuring adherence to client’s SLAs.
- Familiarity with Linux platform and experience in Linux app development is desirable.
- Experienced in DevOps, skilled in Git, CI/CD pipelines (Jenkins, Travis CI, GitLab CI), and containerization (Docker, Kubernetes).
- Experience in Generative Ai would be a huge plus.
- AI compiler/runtime skills would be a huge plus.
- Open-source Contribution is a huge plus. Experience in contributing to open-source AI projects or utilizing open-source AI frameworks is beneficial.
- Strong problem-solving and analytical skills, with experience in optimizing AI algorithms for performance and scalability.
- Familiar with Agile methodologies, adept at collaborative teamwork. Experience in Agile development of AI-based solutions is advantageous, ensuring efficient project delivery through iterative development processes.
- Utilize expertise in AI/ML and Data Science to develop and deploy AI models in production environments, ensuring scalability, reliability, and efficiency.
- Implement and optimize machine learning algorithms, neural networks, and statistical modeling techniques to solve complex problems.
- Hands-on experience in developing and deploying large language models (LLMs) in production environments, with a good understanding of distributed systems, microservice architecture, and REST APIs.
- Collaborate with cross-functional teams to integrate MLOps pipelines with CI/CD tools for continuous integration and deployment.
- Stay updated with the latest advancements in AI/ML technologies and contribute to the development and improvement of AI frameworks and libraries.
- Communicate technical concepts effectively to non-technical stakeholders, demonstrating excellent communication and interpersonal skills.
- Ensure compliance with industry best practices and standards in AI engineering, maintaining high standards of code quality, performance, and security.
- Experience in using container orchestration platforms such as Kubernetes to deploy and manage machine learning models in production environments.
IBM
Business Development
Business Information Systems
CRM
Data Management
Software
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