BILLIGENCE ASIA PTE. LTD.
Minimum 5 years’ experience leading the design, development, and deployment of scalable AI/ML and GenAI solutions.
Key Responsibilities
· Architect and develop scalable GenAI pipelines, APIs, and microservices for real-time and batch AI applications using frameworks such as
FastAPI, Ray, or LangServe. · Design robust prompt strategies for instruction-following, reasoning, and multi-turn conversations, with a focus on RAG architectures for personalized, domain-specific use cases. · Lead embedding model selection and tuning to optimize semantic search and RAG performance. · Oversee LLM Ops workflows, including model orchestration, evaluation, deployment, rollback strategies, and monitoring in production environments. · Drive model fine-tuning efforts to customize LLMs for proprietary datasets and regulated industries. · Establish and govern AI testing frameworks, covering functional testing, regression testing, hallucination detection, safety filters, and output quality assessment. · Implement enterprise-grade observability, lineage tracking, and CI/CD automation using tools such as
MLflow, Databricks, Azure ML, or Vertex AI . · Lead continuous improvement initiatives based on telemetry, user feedback, and cost-performance trade-offs. · Demonstrate expertise in
Python , with deep proficiency in GenAI frameworks, vector search systems, and MLOps toolchains. Qualifications · Minimum 5 years’ experience architecting and deploying scalable AI/ML and GenAI solutions in enterprise environments. · Deep expertise in machine learning, deep learning, and generative AI technologies, including hands-on experience with frameworks like
TensorFlow, PyTorch , and modern
LLM
orchestration tools. · Strong familiarity with cloud platforms ( AWS, Azure, GCP ) and MLOps practices for end-to-end machine learning lifecycle management. · Demonstrated leadership in managing agile, cross-functional teams and collaborating with stakeholders. · Significant experience in prompt engineering and prompt design for LLMs and GenAI applications. · Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field; advanced degrees or certifications (e.g., Azure AI Engineer) are advantageous. · Experience with personalization, recommendation systems, or conversational AI is highly desirable.
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FastAPI, Ray, or LangServe. · Design robust prompt strategies for instruction-following, reasoning, and multi-turn conversations, with a focus on RAG architectures for personalized, domain-specific use cases. · Lead embedding model selection and tuning to optimize semantic search and RAG performance. · Oversee LLM Ops workflows, including model orchestration, evaluation, deployment, rollback strategies, and monitoring in production environments. · Drive model fine-tuning efforts to customize LLMs for proprietary datasets and regulated industries. · Establish and govern AI testing frameworks, covering functional testing, regression testing, hallucination detection, safety filters, and output quality assessment. · Implement enterprise-grade observability, lineage tracking, and CI/CD automation using tools such as
MLflow, Databricks, Azure ML, or Vertex AI . · Lead continuous improvement initiatives based on telemetry, user feedback, and cost-performance trade-offs. · Demonstrate expertise in
Python , with deep proficiency in GenAI frameworks, vector search systems, and MLOps toolchains. Qualifications · Minimum 5 years’ experience architecting and deploying scalable AI/ML and GenAI solutions in enterprise environments. · Deep expertise in machine learning, deep learning, and generative AI technologies, including hands-on experience with frameworks like
TensorFlow, PyTorch , and modern
LLM
orchestration tools. · Strong familiarity with cloud platforms ( AWS, Azure, GCP ) and MLOps practices for end-to-end machine learning lifecycle management. · Demonstrated leadership in managing agile, cross-functional teams and collaborating with stakeholders. · Significant experience in prompt engineering and prompt design for LLMs and GenAI applications. · Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field; advanced degrees or certifications (e.g., Azure AI Engineer) are advantageous. · Experience with personalization, recommendation systems, or conversational AI is highly desirable.
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