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.

What you will do :

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.