AI Engineer (Agentic AI)

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.