- Build and integrate chat UIs, agent libraries, and persona playgrounds using React/TypeScript

- Develop backend services with .NET Core . Python to manage prompts, agent logic, and sessions

- Implement LangChain or Semantic Kernel-based agent flows (chains, tools, memory, embeddings)

- Integrate with Azure OpenAI (GPT-4/4o), Azure Cognitive Search, and blob/file data for grounding

- Enable prompt versioning, history tracking, and persona configuration

- Build REST APIs for agents, chat sessions, user roles, and workspace management.

Must have skill sets :

- Python, React/Typescript, .Net Core, Azure OpenAI, Rest APIs.

About the Role :

We are seeking a talented and motivated Python AI Engineer to join our team and contribute to the design, development, and deployment of AI-powered solutions. In this role, you will work with data scientists, ML engineers, and product teams to implement intelligent systems that solve real-world problems using state-of-the-art machine learning and deep learning technologies.

Key Responsibilities :

- Design and implement scalable machine learning and deep learning models using Python.

- Develop end-to-end AI pipelines including data preprocessing, model training, evaluation, and deployment.

- Collaborate with data scientists and backend engineers to integrate AI models into production systems.

- Optimize model performance and ensure models are efficient, explainable, and maintainable.

- Work with large-scale datasets using libraries such as Pandas, NumPy, and PySpark.

- Leverage frameworks like TensorFlow, PyTorch, or Scikit-learn for model development.

- Use containerization tools (e.g., Docker) and cloud platforms (e.g., AWS, GCP, Azure) for model deployment.

- Stay current with the latest AI research and industry trends to suggest improvements and innovation.

Required Qualifications :

- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field.

- Strong proficiency in Python and its AI/ML ecosystem (NumPy, Pandas, Scikit-learn, etc.).

- Hands-on experience with one or more deep learning frameworks (TensorFlow, PyTorch, etc.).

- Experience in building and deploying machine learning models into production environments.

- Familiarity with REST APIs, microservices, and integration techniques.

- Strong understanding of data structures, algorithms, and software engineering principles.

- Solid grasp of statistics, probability, and ML concepts.