AI Engineer - LLMs & Agentic AI (Contract 6 months) at Ivy Mobility
Company Description:
Ivy Mobility is a rapidly growing enterprise SaaS company specializing in the Consumer Goods sector, delivering cutting-edge solutions to enhance customer experiences and optimize field operations. Businesses leverage Ivy’s cloud-based technologies for retail execution, direct store delivery, and distribution management, streamlining operations and driving revenue growth at the shelf. With support for diverse sales channels, Ivy’s solutions connect suppliers, distributors, and retailers while enabling quick and efficient implementations in just 8-12 weeks. Headquartered in Singapore with a global footprint, Ivy Mobility supports over 55,000 users and processes 100 million mobile transactions monthly across more than 20 countries.
About the Role:
We are seeking a highly skilled AI Engineer with strong hands-on experience in Large Language Models (LLMs) and Agentic AI systems to join our team. The ideal candidate will have 2+ years of direct LLM experience, 1+ year building Agentic AI solutions, and a proven track record using open-source AI frameworks and deploying multi-agent architectures in production environments.
This role will collaborate closely with Product, Engineering, and Data Science teams to design, build, and optimize next-gen GenAI solutions for real-world business problems.
Key Responsibilities:
Design, develop, and deploy LLM-powered applications, including fine-tuning, RAG pipelines, and prompt engineering.
Build and optimize Agentic AI systems, including multi-agent orchestration, tool integrations, and workflow automation.
Architect and implement multi-agent frameworks using open-source toolkits such as LangChain, LlamaIndex, Haystack, AutoGen, CrewAI, Semantic Kernel, or related alternatives.
Develop scalable backend services to operationalize AI models using APIs, microservices, and event-driven workflows.
Evaluate, integrate, and customize open-source LLMs (Llama, Mistral, DeepSeek, Phi, etc.) for domain-specific tasks.
Implement RAG architectures using vector databases (Pinecone, Milvus, Weaviate, Chroma, etc.).
Deploy and maintain models using containerized environments (Docker, Kubernetes) and CI/CD pipelines.
Conduct benchmarking, model evaluation, and performance tuning for latency, accuracy, and cost efficiency.
Work cross-functionally to understand business needs and translate them into scalable AI solutions. 10.Ensure security, governance, compliance, and responsible AI practices across deployments.
Required Skills & Experience:
3–5 years of hands-on experience in AI/ML engineering or related fields.
2+ years working directly with LLMs (OpenAI, Anthropic, Meta, Mistral, or similar).
1+ year building Agentic AI systems with multi-agent orchestration.
Experience with open-source frameworks such as LangChain, LlamaIndex, HuggingFace Transformers, CrewAI, AutoGen, or equivalents.
Proven experience deploying multi-agent solutions in production.
Strong software engineering skills in Python (preferred), with experience in FastAPI/Flask for serving models.
Solid understanding of vector databases, embeddings, RAG, and prompt engineering best practices.
Experience working with cloud platforms (Azure, AWS, GCP) and MLOps stacks.
Proficiency with Docker, Kubernetes, GitHub Actions/GitLab CI, or other DevOps tooling.
Experience with monitoring and observability tools for AI workloads.
Good-to-Have Skills:
Experience fine-tuning or training custom LLMs.
Familiarity with Retrieval-Augmented Generation optimizations (caching, routing, re-ranking).
Background in microservices, event-driven architectures, and performance engineering.
Knowledge of enterprise AI governance, data privacy, and compliance frameworks.
Exposure to .NET or enterprise system integration is a plus.
Understanding of multi-modal AI (vision + LLMs) is an advantage
Education:
Bachelor’s or Master’s degree in Computer Science, Engineering, AI/ML, Data Science, or equivalent practical experience.
What We Offer:
Opportunity to build and scale cutting-edge AI systems in a production environment.
Work with the latest LLMs, open-source frameworks, and agentic architectures.
A collaborative environment with strong technical leadership and room for innovation.
Competitive compensation and career growth in the GenAI space.