Project Overview
We are looking for a Senior AI Engineer to join our AI Engineering team to design, develop, and deploy enterprise-grade AI solutions for global customers. You will work on cutting-edge AI initiatives, including Generative AI, Large Language Models (LLMs), AI Agents, Retrieval-Augmented Generation (RAG), Machine Learning, and MLOps.
This role requires hands-on technical expertise, strong software engineering skills, and the ability to translate business problems into scalable AI solutions running on cloud platforms such as AWS.
Job Description
Key Responsibilities
- Design, develop, and deploy AI/ML solutions for enterprise applications.
- Build and optimize Generative AI applications using Large Language Models (LLMs).
- Design and implement Retrieval-Augmented Generation (RAG) pipelines.
- Develop AI Agents and workflow automation using modern AI frameworks.
- Fine-tune, evaluate, and optimize foundation models where applicable.
- Integrate AI capabilities into existing enterprise systems through APIs and microservices.
- Build scalable AI services on AWS cloud infrastructure.
- Develop and maintain ML pipelines and MLOps workflows for model training, deployment, monitoring, and lifecycle management.
- Collaborate with Product Managers, Solution Architects, Data Engineers, and Software Engineers throughout the SDLC.
- Evaluate emerging AI technologies and recommend suitable architectures and tools.
- Mentor junior engineers and promote AI engineering best practices.
- Ensure AI solutions meet security, scalability, performance, and compliance requirements.
Qualifications
Experience
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- 5+ years of software engineering experience.
- At least 3 years of hands-on experience developing AI/ML solutions.
- Proven experience delivering production-grade AI applications.
Technical Skills
Programming
- Python (mandatory)
- Java or C# is a plus
- SQL
AI / Machine Learning
- Machine Learning and Deep Learning fundamentals
- Large Language Models (OpenAI, Claude, Gemini, Llama, Mistral, etc.)
- Prompt Engineering
- Retrieval-Augmented Generation (RAG)
- AI Agents
- Vector Databases (Pinecone, Milvus, Weaviate, Chroma, FAISS)
- Model evaluation and optimisation
Frameworks
- LangChain
- LangGraph
- LlamaIndex
- Hugging Face
- PyTorch or TensorFlow
- Scikit-learn
Cloud & MLOps
- AWS (SageMaker, Bedrock, Lambda, ECS/EKS, S3, API Gateway)
- Docker
- Kubernetes
- CI/CD
- MLflow
- Git
Data
- PostgreSQL
- MongoDB
- Redis
- Vector databases
Preferred Qualifications
- Experience with Amazon Bedrock or Azure AI Foundry.
- Experience deploying LLMs in production environments.
- Knowledge of AI governance, Responsible AI, and AI security.
- Experience with multi-agent systems and orchestration frameworks.
- Experience building AI copilots, chatbots, or intelligent automation platforms.
- AWS Certified Machine Learning – Specialty or AWS Certified AI Practitioner is a plus.
- Experience in Healthcare, Financial Services, or other regulated industries is highly desirable.
Soft Skills
- Strong analytical and problem-solving skills.
- Excellent communication and collaboration abilities.
- Ability to work independently and lead technical initiatives.
- Passion for continuous learning and emerging AI technologies.
- Good written and spoken English.
Others / Benefits
- Flexible working hours and hybrid working model.
- Opportunity to work on enterprise AI, GenAI, and Agentic AI projects for global clients.
- Sponsored training and certifications (AWS, AI/ML, Kubernetes, Cloud).
- Access to modern AI platforms and enterprise-grade GPU/cloud environments.
- Clear technical and leadership career progression.
- Collaborative, innovative, and international working environment.
