SENIOR AI ENGINEER
SENIOR AI ENGINEER
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Job Description
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 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.
Preferred Qualifications
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.
Employment Type
Full Time
