AI Engineer

About SimuPhish

SimuPhish is an AI-driven Human Risk Management and Phishing Simulation platform built to help organizations strengthen their human firewall through realistic simulations, gamified awareness, and data-driven insights. We empower employees to become the strongest defence against cyber threats. 

💡 Role Overview

We are looking for a hands-on AI Engineer with 2–3 years of experience to design, build, deploy, and scale AI-powered solutions. This role requires strong foundations in machine learning, modern AI (including GenAI), and solid software engineering practices to deliver production-ready AI systems. 

🧩 Key Responsibilities

• Design, develop, and integrate AI/ML and Generative AI models into production systems. 
• Build and optimize ML pipelines covering data preparation, training, evaluation, and inference. 
• Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases and advanced prompt engineering techniques. 
• Develop and deploy scalable AI services using microservices architecture. 
• Deploy AI solutions on cloud environments using containerization and CI/CD pipelines. 
• Set up monitoring, logging, and performance tracking for deployed AI models. 
• Build and integrate AI capabilities through APIs and system-level integrations. 
• Develop and experiment with AI agents capable of multi-step reasoning, tool usage, and autonomous workflows. 
• Collaborate with cross-functional teams to translate business problems into AI-driven solutions. 
• Ensure code quality, security, scalability, and maintainability through best software engineering practices. 

Essential Skills & Technical Expertise

1. Programming Proficiency

• Strong proficiency in Python for developing, training, and integrating AI/ML and GenAI models. 

2. Machine Learning & Modern AI

• Solid understanding of core ML algorithms and deep learning concepts. 
• Practical experience with modern architectures, especially Transformers. 
• Hands-on experience with deep learning frameworks such as TensorFlow or PyTorch. 

3. Vector Databases, RAG & Prompt Engineering

• Experience working with vector databases for semantic search and retrieval. 
• Ability to design and implement RAG pipelines. 
• Advanced prompt engineering skills to optimize LLM outputs. 

4. Deployment, MLOps & AI Integration

• Experience deploying AI models in cloud environments. 
• Hands-on knowledge of Docker, Kubernetes, and CI/CD pipelines. 
• Understanding of MLOps practices including model versioning, monitoring, and retraining. 
• Experience with API-based AI integration and AI calling mechanisms. 

5. Data Management

• Experience handling large datasets including data cleaning and feature engineering. 
• Working knowledge of SQL, NoSQL, and vector databases. 
• Familiarity with big data tools and data pipelines is a plus. 

6. Microservices

• Experience designing and deploying AI-driven microservices. 
• Understanding of service-to-service communication and scalability concerns. 

7. AI Agents & Generative AI

• Hands-on experience building AI agents using LLMs. 
• Understanding of multi-step reasoning, tool usage, and autonomous AI workflows. 
• Exposure to other Generative AI models such as image or video generation is a plus. 

8. Software Engineering & System Design

• Strong grasp of software engineering best practices including testing, version control, and documentation. 
• Ability to design robust, scalable, and secure AI systems. 

✅ Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. 
• 2–3 years of relevant hands-on experience in AI/ML engineering roles. 

🌟 Why Join Us

• Work with a global cybersecurity brand making a real impact. 
• Remote flexibility with a collaborative and innovative team culture. 
• Opportunity to experiment, innovate, and grow with creative freedom. 
• Inclusive, supportive environment that values ideas and individuality.