AI Engineer

• 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.

• Data Science and Generative AI Experience:

• 7+ years of experience in Data Science and Generative AI.
• Background in machine learning, deep learning, and natural language processing.
• Model Experience:

• Hands-on experience with transformer-based and diffuser-based models (e.g., BERT, GPT, T5, Llama, Stable diffusion).
• Desirable experience in testing AI algorithms and models.
• Traditional AI Methodologies:

• Robust background in traditional AI methodologies, including machine learning and deep learning frameworks.
• Familiarity with model serving platforms such as TGIS and vLLM.
• Programming Proficiency:

• Proficiency in Python, C++, Go, Java.
• Experience with relevant ML libraries (e.g., TensorFlow, PyTorch) for developing production-grade quality products.
• Full-Stack Development:

• Proficient in full-stack development, including frontend (HTML, CSS, JavaScript) and backend (Django, Flask, Spring Boot).
• Experience integrating AI tech into full-stack projects.
• Data Handling Skills:

• Skilled in integrating, cleansing, and shaping data.
• Expertise in various databases including open-source databases like MongoDB, CouchDB, CockroachDB.
• Data Pipeline Architectures:

• Proficient in developing optimal data pipeline architectures for AI applications.
• Ensuring adherence to client’s SLAs.
• DevOps Experience:

• Experienced in DevOps practices.
• Skills in Git, CI/CD pipelines (Jenkins, Travis CI, GitLab CI), and containerization (Docker, Kubernetes).
• Open-Source Contribution:

• Open-source Contribution is a plus.
• Experience in contributing to open-source AI projects or utilizing open-source AI frameworks.
• Problem-Solving Skills:

• Strong problem-solving and analytical skills.
• Experience in optimizing AI algorithms for performance and scalability.
• AI Compiler/Runtime Skills:

• AI compiler/runtime skills would be a plus.
• Agile Methodologies:

• Familiarity with Agile methodologies.
• Experience in Agile development of AI-based solutions.
• Ensuring efficient project delivery through iterative development processes

• AI/ML and Data Science Proficiency:

• 7+ years of expertise in AI/ML and Data Science to develop and deploy AI models in production environments, ensuring scalability, reliability, and efficiency.
• Algorithm Implementation and Optimization:

• Proven ability to implement and optimize machine learning algorithms, neural networks, and statistical modeling techniques to solve complex problems effectively.
• Large Language Models (LLMs) Development:

• Hands-on experience in developing and deploying large language models (LLMs) in production environments.
• Proficiency in distributed systems, microservice architecture, and REST APIs.
• MLOps Integration:

• Experience in collaborating with cross-functional teams to integrate MLOps pipelines with CI/CD tools for continuous integration and deployment, ensuring seamless integration of AI/ML models into production workflows.
• Continuous Learning and Contribution:

• Demonstrated commitment to staying updated with the latest advancements in AI/ML technologies.
• Proven ability to contribute to the development and improvement of AI frameworks and libraries.
• Effective Communication:

• Strong communication skills with the ability to communicate technical concepts effectively to non-technical stakeholders.
• Demonstrated excellence in interpersonal skills, fostering collaboration across diverse teams.
• Adherence to Industry Standards:

• Proven track record of ensuring compliance with industry best practices and standards in AI engineering.
• Maintained high standards of code quality, performance, and security in AI projects.
• Container Orchestration:

• Experience in using container orchestration platforms such as Kubernetes to deploy and manage machine learning models in production environments, ensuring efficient scalability and management of AI infrastructure.

IBM