HCL SINGAPORE PTE. LTD.
Summary
We are seeking a highly skilled and passionate AI Engineer with expertise in Generative AI (GenAI) to develop and implement cutting-edge AI solutions across cloud-native environments. This role combines hands-on technical engineering with strategic leadership, enabling customers to translate AI innovation into scalable products—from ideation to MVP and beyond. Responsibilities Design, build, and deploy GenAI-driven applications using frameworks such as LangChain, OpenAI API, and Hugging Face Transformers. Architect and implement scalable solutions within containerized, microservices-based cloud-native environments (e.g., Kubernetes, OpenShift). Integrate GenAI models and services seamlessly into existing platforms and services. Collaborate with partner teams (Google, Microsoft, AWS, IBM, etc.) on advanced AI solution development. Refactor and modernize legacy systems with 12-factor principles and cloud-native best practices. Drive innovation in GenAI adoption through internal R&D, PoCs, and pilot programs. Stay ahead of AI trends and apply the latest research into practical applications. Provide expert consultation on GenAI architecture, deployment strategies, and business use case alignment. Facilitate technical workshops, presentations, and strategic planning sessions with clients. Serve as a trusted technical advisor on AI/ML integration, helping clients unlock value from AI technologies. Requirements Bachelor’s degree in Computer Science, Information Technology, or a related field 5 years of software engineering experience, with strong programming skills in Java and Python Deep expertise in Generative AI frameworks, including LangChain, OpenAI, Hugging Face Transformers. Strong foundation in Natural Language Processing (NLP): tokenization, embeddings, transformers, LLMs. Experience with prompt engineering and optimizing AI model interactions. Proven success designing and deploying cloud-native solutions using Kubernetes, Docker, and microservices. Strong experience with Agile and DevOps practices, working in Jira-based environments. Hands-on experience with MLOps (CI/CD for ML, model versioning, monitoring). Proficiency in data engineering for AI: ETL, feature pipelines, data labeling, quality control. Knowledge of vector databases (e.g., Pinecone, FAISS, Weaviate) and retrieval-augmented generation (RAG) techniques. Cloud certifications from AWS, Azure, or Google Cloud Platform (GCP).
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We are seeking a highly skilled and passionate AI Engineer with expertise in Generative AI (GenAI) to develop and implement cutting-edge AI solutions across cloud-native environments. This role combines hands-on technical engineering with strategic leadership, enabling customers to translate AI innovation into scalable products—from ideation to MVP and beyond. Responsibilities Design, build, and deploy GenAI-driven applications using frameworks such as LangChain, OpenAI API, and Hugging Face Transformers. Architect and implement scalable solutions within containerized, microservices-based cloud-native environments (e.g., Kubernetes, OpenShift). Integrate GenAI models and services seamlessly into existing platforms and services. Collaborate with partner teams (Google, Microsoft, AWS, IBM, etc.) on advanced AI solution development. Refactor and modernize legacy systems with 12-factor principles and cloud-native best practices. Drive innovation in GenAI adoption through internal R&D, PoCs, and pilot programs. Stay ahead of AI trends and apply the latest research into practical applications. Provide expert consultation on GenAI architecture, deployment strategies, and business use case alignment. Facilitate technical workshops, presentations, and strategic planning sessions with clients. Serve as a trusted technical advisor on AI/ML integration, helping clients unlock value from AI technologies. Requirements Bachelor’s degree in Computer Science, Information Technology, or a related field 5 years of software engineering experience, with strong programming skills in Java and Python Deep expertise in Generative AI frameworks, including LangChain, OpenAI, Hugging Face Transformers. Strong foundation in Natural Language Processing (NLP): tokenization, embeddings, transformers, LLMs. Experience with prompt engineering and optimizing AI model interactions. Proven success designing and deploying cloud-native solutions using Kubernetes, Docker, and microservices. Strong experience with Agile and DevOps practices, working in Jira-based environments. Hands-on experience with MLOps (CI/CD for ML, model versioning, monitoring). Proficiency in data engineering for AI: ETL, feature pipelines, data labeling, quality control. Knowledge of vector databases (e.g., Pinecone, FAISS, Weaviate) and retrieval-augmented generation (RAG) techniques. Cloud certifications from AWS, Azure, or Google Cloud Platform (GCP).
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