The Artificial Intelligence Engineer plays a critical role in designing, building, and operationalizing AI-driven solutions that create measurable business value. This role focuses on developing production-ready machine learning and generative AI systems that transform data into intelligent products, automation, and insights.
The AI Engineer works across the full AI lifecycle — from data exploration and model development to deployment, monitoring, and continuous improvement. This role partners closely with product managers, software engineers, data engineers, and business stakeholders to translate complex business challenges into scalable AI solutions.
Beyond model development, the AI Engineer contributes to building reliable AI platforms and reusable components, ensuring that AI capabilities can be deployed securely, ethically, and efficiently across the organization.
The role requires curiosity, technical rigor, and a strong drive to apply cutting-edge technologies such as Generative AI, Large Language Models (LLMs), and intelligent automation to real-world business problems.
WHAT IS THE JOB LIKE
AI Solution Development & Deployment
- Design, develop, and deploy machine learning and generative AI models to solve real-world business challenges
- Build scalable AI pipelines for data preprocessing, feature engineering, model training, evaluation, and deployment
- Develop and integrate AI-powered services into applications, APIs, and internal platforms
- Contribute to the implementation of production-ready AI systems using modern MLOps practices
- Ensure models are reliable, scalable, and maintainable through proper monitoring and lifecycle management
Generative AI & Advanced AI Systems
- Develop solutions using LLMs, prompt engineering, Retrieval Augmented Generation (RAG), and AI agents
- Integrate Generative AI capabilities into internal tools, workflows, and customer-facing products
- Evaluate and experiment with emerging AI models, frameworks, and architectures
- Optimize model performance, latency, and cost efficiency in real-world deployments
- Implement evaluation frameworks to measure generative AI output quality and reliability
Translating Business Problems into AI Solutions
- Collaborate with product teams and stakeholders to identify high-impact AI opportunities
- Translate business requirements into data science and machine learning solutions
- Conduct exploratory data analysis and feature engineering to uncover insights and build predictive capabilities
- Evaluate trade-offs between model accuracy, complexity, cost, and operational constraints
- Deliver solutions that demonstrate clear, measurable business value
Data Engineering & AI Infrastructure Collaboration
- Work closely with data engineers to ensure reliable data pipelines and high-quality datasets
- Contribute to building reusable AI infrastructure and components
- Utilize cloud platforms and distributed computing frameworks for scalable model development
- Support integration of AI capabilities within broader data and software ecosystems
AI Governance, Ethics & Reliability
- Ensure all AI systems adhere to organizational standards for data privacy, security, and responsible AI
- Maintain transparency and documentation around model design, training data, and evaluation results
- Monitor models in production to detect drift, bias, and performance degradation
- Implement best practices for explainability, fairness, and traceability in AI systems
Innovation & Continuous Learning
- Stay current with developments in Artificial Intelligence, Generative AI, and Machine Learning
- Explore new AI tools, frameworks, and research to improve solution capabilities
- Participate in internal innovation initiatives, proof-of-concept development, and technical discussions
- Contribute to knowledge sharing, documentation, and internal AI capability building
WHO ARE YOU
Must-Haves
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field
- 1–4 years of experience in machine learning, artificial intelligence, or applied data science
- Strong programming skills in Python and SQL
- Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn
- Familiarity with Generative AI technologies, LLM APIs, or prompt engineering
- Experience working with cloud platforms such as Azure, AWS, or GCP
- Understands model evaluation, experimentation, and performance optimization
- Strong analytical thinking and problem-solving skills
- Ability to communicate technical concepts to both technical and non-technical stakeholders
