AI Engineer at Zenika Singapore

Singapore, Singapore, Singapore -

Full Time

Start Date

Immediate

Expiry Date

15 Oct, 26

Salary

0.0

Posted On

17 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, TypeScript, LLMs, RAG, LangChain, LangGraph, LlamaIndex, CrewAI, Vector Databases, Docker, Kubernetes, AWS Bedrock, GCP Vertex AI, Azure OpenAI, TDD, CI/CD

Industry

IT Services and IT Consulting

Description

Is there a Zenika in you? Let’s talk skills and passion first. You thrive on building intelligent, robust systems and production-grade AI pipelines. You are a software engineer at heart who views AI models as powerful tools, but you possess a craftsman’s mindset—focusing on simplicity, performance, and ethical safety. You care deeply about system reliability, latency, and clean code. Whether you are optimizing a Retrieval-Augmented Generation (RAG) system, coordinating multi-agent workflows, or managing vector databases, you take pride in software that is highly maintainable under the hood and delivers real, measurable value to production environments. Your Role as a Zenika Consultant: As a consultant, you'll work on strategic client engagements. You will design, build, and deploy reliable AI-powered applications while collaborating closely with client engineering teams to: Architect & Build: Design and develop end-to-end generative AI systems, utilizing LLMs, semantic search, and robust backend integrations (primarily in Python or TypeScript). Agentic Workflows: Implement autonomous AI agents and multi-agent frameworks capable of executing complex, multi-step actions and tool calling. Data & Vector Management: Structure, optimize, and maintain vector databases (e.g., Qdrant, Pinecone, Chroma, or PGVector) and advanced embedding/chunking strategies. Evaluation & Guardrails: Design robust metrics to evaluate model performance (accuracy, latency, costs) and establish security guardrails to mitigate hallucinations and data leaks. Engineering Standards: Apply solid software engineering practices to AI development, including unit testing, test-driven development (TDD), rigorous code reviews, and living documentation. Cloud & Automation: Deploy cloud-native AI applications using Docker, Kubernetes, and cloud-native AI services (AWS Bedrock, GCP Vertex AI, or Azure OpenAI). Leadership: Help clients navigate the AI hype by identifying high-impact use cases, establishing best practices, and mentoring client engineering teams to become self-sufficient. What You Bring Experience: 3-8 years of professional software development experience, with a solid focus on building and deploying AI/LLM-integrated applications. AI/LLM Application Stack: Hands-on experience with orchestration libraries such as LangChain, LangGraph, LlamaIndex, or CrewAI. Software Craftsmanship: A passion for clean code (SOLID, DRY), strong proficiency in Python, and writing robust test suites (e.g., PyTest). Data & Vector Stores: Direct experience working with relational databases and vector search engines. Cloud & DevOps: Hands-on experience with Docker, CI/CD pipelines, and cloud environments (AWS, GCP, or Azure). Consulting Mindset: Comfort working with diverse clients, translating complex AI concepts to non-technical stakeholders, and adapting quickly to different team cultures. About Zenika Founded by developer Carl Azoury, Zenika is a consultancy built around community, transparency, and craftsmanship. Our passionate team advises clients with expertise in open-source technologies and modern solutions. Why Join Zenika? Work with a global client base across 11 locations, accessing 28,000+ Zenika-led training sessions worldwide Partner with industry leaders like Google Cloud and Scrum.org, and engage in research, open-source work, and conferences outside client projects Connect and grow with fellow experts through our annual TechnoZaures, sharing skills and knowledge Ready to code your story with us? Apply NOW!

Responsibilities

Design, build, and deploy production-grade generative AI systems and agentic workflows for strategic clients. Collaborate with engineering teams to implement vector management, evaluation metrics, and cloud-native AI deployments.