AI Engineer (Agentic AI)

Seeking an experienced AI Engineer (46 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

? 46 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.