We are seeking a Senior AI Engineer with expertise in AI agent development to design, build, and deploy intelligent agents that operate autonomously in digital environments. This role involves creating AI agents capable of advanced reasoning, planning, decision-making, and interactive tasks using large language models (LLMs), multi-agent systems, and simulation frameworks. You will work at the frontier of AI development, building agents that learn from data, communicate with users or other systems, and adapt to complex, dynamic environments. In addition, you will champion MLOps and LLMOps best practices to ensure our AI applications and models are robustly trained, deployed, and maintained at scale.
● Design and implement AI agents using LLMs, planning algorithms, and decision-making frameworks.
● Develop agent architectures that support autonomy, interactivity, and effective task completion.
● Integrate agents into applications, APIs, or workflows (e.g., chatbots, copilots, automation tools) to deliver user-facing functionality.
● Implement MLOps and LLMOps (Large Language Model Ops) best practices – including pipeline design, CI/CD for model deployment, and performance monitoring – to ensure scalable and reliable AI agent operations.
● Lead strategic initiatives related to the model lifecycle, such as automated retraining, model versioning, and deployment workflows, to continuously improve model performance and reliability.
● Collaborate with researchers, engineers, and product teams to iterate on agent capabilities and ensure alignment with product goals.
● Optimize agent behaviour through feedback loops, reinforcement learning techniques, or user interaction data to improve performance over time.
● Monitor agent performance, conduct evaluations, and implement safety/guardrail mechanisms to uphold reliability, ethical standards, and user trust.
● Contribute to the architecture of large-scale systems, providing technical leadership and influencing product direction with AI-driven solutions.
● Maintain thorough documentation of agent logic, design decisions, pipelines, and dependencies for future reference and knowledge sharing.
Requirements
● Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related field.
● Python Development: 5+ years of hands-on software development experience, with strong software engineering skills in Python (including PySpark) and full lifecycle development (design, coding, testing, deployment).
● Machine Learning Expertise: 5+ years of experience developing and deploying machine learning models and workflows, with a solid understanding of ML principles, model training, fine-tuning, and evaluation.
● Generative AI Leadership: Hands-on experience leading ML Generative AI initiatives from concept to deployment.
● AI/ML Frameworks: Strong programming skills in Python and experience with AI/ML frameworks and libraries such as LangChain, OpenAI API, Hugging Face, PyTorch, and scikit-learn.
● MLOps & LLMOps: Solid understanding and experience implementing MLOps and LLMOps practices, including automated ML pipelines, CI/CD for models, model monitoring, and lifecycle management.
● Containerization & Orchestration: Proven experience designing and deploying AI solutions using Docker and Kubernetes, with knowledge of scalable architectures.
● Agent-Based Systems: Understanding of agent-based modeling, reinforcement learning, multi-agent systems, and planning algorithms.
● LLM Applications: Experience building applications with large language models (LLMs) or generative AI, integrating them into real-world products and services.
● System Integration: Familiarity with API development, backend services, and deployment pipelines for ML models.
● Work Style: Ability to work independently in experimental, fast-paced environments, rapidly prototyping and iterating on ideas.
● Experience with frameworks for autonomous agents (e.g., AutoGPT, LangChain, Langgraph Agents) and multi-agent system orchestration.
● Background in human-AI interaction, conversational UX, or simulation environments that involve AI agents.
● Knowledge of vector databases, prompt engineering, or retrieval-augmented generation (RAG) to enhance LLM-driven agent performance.
● Hands-on experience with MLOps and LLMOps tools and platforms (e.g., MLflow, TensorFlow Extended, or Kubeflow) and managing large-scale model deployments.
● Contributions to open-source AI projects or a portfolio showcasing agent-based systems and AI/ML projects.
