Job Description
A reputable organisation in the technology and innovation space is seeking an experienced Senior AI Engineer & Machine Learning Engineer to design, build, and deploy autonomous AI agents across complex digital environments. This role focuses on applying large language models (LLMs), agentic frameworks, and multi-agent systems to deliver production-ready AI solutions with real-world business impact. The position involves close collaboration with cross-functional teams to develop scalable, secure, and high-performing AI-driven products.
Responsibilities
- Design and implement autonomous AI agents using LLMs, planning algorithms, and decision-making frameworks.
- Develop agent architectures that support autonomy, interactivity, and reliable task execution.
- Integrate AI agents into applications, APIs, and workflows such as chatbots, copilots, and automation tools.
- Implement MLOps and LLMOps practices, including CI/CD pipelines, model monitoring, and performance evaluation.
- Lead initiatives around model lifecycle management, including automated retraining, versioning, and deployment workflows.
- Collaborate with researchers, engineers, and product teams to align agent capabilities with business and product goals.
- Optimize agent behaviour using feedback loops, reinforcement learning techniques, and user interaction data.
- Monitor performance and implement safety, reliability, and ethical guardrails for AI systems.
- Contribute to large-scale system architecture and provide technical leadership on AI-driven solutions.
- Maintain clear documentation of system design, pipelines, dependencies, and decision rationale.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related field.
- Minimum of 5 years of hands-on Python development experience, including full software development lifecycle and PySpark exposure.
- At least 5 years of experience building, training, fine-tuning, and deploying machine learning models in production.
- Proven experience leading Generative AI initiatives from concept through deployment.
- Strong working knowledge of AI/ML frameworks and libraries such as LangChain, OpenAI API, Hugging Face, PyTorch, and scikit-learn.
- Practical experience with MLOps and LLMOps, including automated pipelines, CI/CD for models, monitoring, and lifecycle management.
- Experience deploying AI solutions with Docker and Kubernetes in scalable environments.
- Understanding of agent-based systems, reinforcement learning, multi-agent architectures, and planning algorithms.
- Experience integrating LLMs into real-world applications and backend systems via APIs and deployment pipelines.
- Ability to work independently in fast-paced, experimental environments with rapid prototyping and iteration.
Preferred Qualifications
- Experience with autonomous agent frameworks such as AutoGPT, LangChain Agents, or LangGraph.
- Background in human–AI interaction, conversational UX, or simulation environments.
- Knowledge of vector databases, prompt engineering, and retrieval-augmented generation (RAG).
- Hands-on experience with MLOps/LLMOps tools such as MLflow, Kubeflow, or TensorFlow Extended.
- Contributions to open-source AI projects or a portfolio demonstrating agent-based systems and AI/ML solutions.

