AI Engineer

About the job

Seeking an experienced AI Engineer (4–6 years) to design, build, and deploy agentic AI solutions using Python and machine learning, with a strong focus on RAG, regression models and data-driven decision systems. Responsibilities • Design and develop autonomous / agentic AI workflows that can plan, reason, and take goal-directed actions using LLMs and tool-calling capabilities. • Implement, train, and optimize regression models (linear, regularized, tree-based, ensemble, and nonlinear regression) for forecasting, recommendation, and optimization use cases. • Build end-to-end pipelines: data ingestion, feature engineering, model training, validation, deployment, and monitoring in production environments. • Develop Python-based services (FastAPI / Flask) to expose models and agents as robust, scalable APIs. • Integrate agents with external tools and systems (databases, REST APIs, vector stores, message queues) to enable complex workflows. • Evaluate model and agent performance using appropriate metrics, perform error analysis, and iteratively improve robustness and reliability. • Collaborate with product, data, and DevOps teams to translate business problems into AI solutions and deliver them to production. • Document designs, experiments, and best practices; contribute to internal libraries and reusable components. Required Skills and Experience • 4–6 years of hands-on experience in AI / ML engineering or data science, including taking models or agents to production. • Strong proficiency in Python and core data / ML stack: NumPy, pandas, scikit-learn; exposure to PyTorch or TensorFlow is a plus. • Solid understanding of regression techniques: o Linear and logistic regression. o Regularization (Ridge, Lasso, Elastic Net). o Tree-based and ensemble methods (Random Forest, Gradient Boosting). • Experience working with LLMs and at least one agentic / LLM framework. • Experience integrating vector databases and retrieval (e.g., RAG setups) is highly desirable. • Good understanding of software engineering practices: Git, testing, code review, CI / CD, and packaging. • Experience deploying ML services on cloud platforms (AWS / Azure / GCP) or containerized environments (Docker, Kubernetes). • Strong problem-solving skills, ability to own features end to end, and comfort working in an agile environment. Nice-to-Have • Experience with time-series regression and forecasting. • Experience with experiment tracking and MLOps tools (MLflow, Weights & Biases, or similar). • Exposure to reinforcement learning or planning algorithms for agentic behaviour. • Experience in domains like fintech, edtech, or SaaS analytics.

Requirements

• Python
• Machine Learning
• Regression Models
• NumPy
• pandas
• scikit-learn

Preferred Technologies

• Python
• Machine Learning
• Regression Models
• NumPy
• pandas
• scikit-learn