Job Description
We are seeking for our client an experienced Senior ML/AI Engineer to build and deploy the intelligent systems that power next-generation enterprise AI solutions. This role focuses on designing production-grade machine learning models, agentic AI architectures, and advanced reasoning systems that support forecasting, consumer intelligence, competitive analysis, and autonomous decision-making. You will work at the intersection of machine learning, large language models, graph technologies, and enterprise-scale data systems, helping transform cutting-edge AI innovations into impactful products.
Requirements
Critical Requirements
- 5+ years of experience in applied Machine Learning or Artificial Intelligence roles.
- Proven experience deploying and maintaining ML models in production environments.
- Strong proficiency in Python.
- Hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
- Strong foundation in statistics, predictive analytics, and time-series forecasting.
- Experience with Large Language Models (LLMs), NLP, and Retrieval-Augmented Generation (RAG).
- Experience building agentic AI systems or multi-agent orchestration frameworks.
- Familiarity with large-scale datasets and distributed computing environments.
- Strong problem-solving and communication skills.
- Previous experience at top-tier AI, infrastructure, developer tooling, data platform, or fintech companies (or companies of similar scale and complexity).
- Experience building and scaling high-impact products in fast-moving environments is strongly preferred.
Important Requirements
- Design, develop, and deploy machine learning models for demand forecasting, time-series prediction, anomaly detection, and sentiment analysis.
- Build and enhance agentic AI systems capable of reasoning across complex data environments and performing autonomous actions.
- Develop and maintain end-to-end ML pipelines, including data preparation, feature engineering, model training, validation, deployment, and monitoring.
- Design and optimize Retrieval-Augmented Generation (RAG) systems and LLM-powered applications.
- Contribute to the architecture and improvement of graph-based AI systems and Graph RAG capabilities.
- Collaborate closely with software engineers to ensure scalable, reliable, and production-ready AI solutions.
- Monitor model performance and implement continuous improvement and retraining strategies.
- Evaluate emerging AI technologies and integrate relevant innovations into products and workflows.
Nice to Have Requirements
- Experience with graph databases, knowledge graphs, or Graph RAG systems.
- Experience in retail analytics, supply chain optimization, or demand forecasting.
- Knowledge of Graph Neural Networks (GNNs).
- Experience with MLOps, model serving infrastructure, and production ML platforms.
- Open-source contributions or published research in AI/ML fields.
- Experience working in fast-paced startup environments.