All Level - AI Engineer (Data Scientist)

Required Skills & QualificationsAI ENGINEERA Bachelor's degree in a technical field such as CS, AI, or Data Science, or equivalent practical experience. A Master’s degree is a plus.2- 3 years of professional experience building and shipping software, with a focus on AI/ML systems.Familiarity with cloud platforms (AWS, Azure, GCP). Proficiency with containerization (Docker, Kubernetes) and modern CI/CD practices for production environments. Experience in building or prototyping features with GenAI capabilities (LLMs, Embeddings, Fine-Tuning) and core NLP concepts.Experience in agentic frameworks (eg. LangChain, MCP, AutoGen) for building LLM-powered applications.Experience with reinforcement learning, especially RLHF or applying RL to agent-based systems.Fluent in English with strong communication skills, especially when engaging with non-technical business stakeholders.The ability to work independently, troubleshoot complex issues, and mentor more junior team members is also expected.Strong problem-solving skills and a data-driven mindset are fundamental.Develop and Implement Evaluation Frameworks: The engineer will be responsible for creating and deploying frameworks to test and benchmark generative AI solutions for both quantitative and qualitative metricsExperience with high-performance computing, GPU optimization, and building for low-latency environments is a strong plus. DATA SCIENTISTBachelor’s degree in Mathematics, Statistics, Data Science, Computer Science, AI, Engineering, or a highly related field is required. A Master’s degree in any of the above disciplines is preferred.Have at least 3 years and above of experience in one or more data science domains such as machine learning, deep learning, natural language processing (NLP), computer vision (CV), optimization, or statistics.Proven experience leading data science workstreams or small projects, and mentoring junior data scientists.Fluent in English with strong communication skills, especially when engaging with non-technical business stakeholders.Experience with MLOps tools and practices (e.g., model packaging, CI/CD for ML, model monitoring, ML pipelines).High proficiency in Python and SQL, along with relevant data science librariesHands-on experience with cloud platforms (e.g., GCP, AWS, Azure) and containerization (e.g., Docker, Kubernetes).Capable of researching, reading publications, and translating research papers into code.Have excellent problem-solving skills. Be proactive and take full responsibility.Ability to participate in communication of technical concepts and collaborate effectively with cross-functional teams.Ability to engage with business stakeholders to gather domain knowledge and requirements, and to communicate timelines, results, and other relevant matters.Familiarity with agile delivery practices is a plus.