Opening For Python & Generative AI

Position Summary: We seek an experienced Software Engineer with 7+ years of hands-on expertise in Python development and Generative AI technologies. The ideal candidate will design, develop, and deploy RESTful APIs and end-to-end GenAI solutions, driving innovation through experimentation in Jupyter notebooks, robust API development, and advanced prompt engineering across leading LLM platforms.

Key Responsibilities:

Develop and maintain REST APIs using Python frameworks such as FastAPI, Flask, or Django REST Framework

Conduct prototyping and experimentation in Jupyter notebooks utilizing ML libraries including scikit-learn, TensorFlow, and PyTorch

Architect and productionize Generative AI pipelines and workflows

Design and optimize prompts, prompt templates, and chain-of-thought strategies for large language models (OpenAI, Anthropic, Hugging Face, etc.)

Integrate vector databases and implement retrieval-augmented generation (RAG) and embedding-based solutions

Containerize applications using Docker and manage deployments via Kubernetes or similar orchestration platforms

Build and maintain CI/CD pipelines for both code and model artifacts using tools like GitLab CI/CD, GitHub Actions, or Cloud Build

Monitor system performance, cost efficiency, accuracy, and ethical compliance; iterate on model evaluation metrics accordingly

Mentor junior engineers on Python best practices, software design principles, and Generative AI methodologies

Required Qualifications:

Bachelors or Master’s degree in Computer Science, Engineering, or a related discipline

Minimum 7 years of professional experience in Python software development

Strong expertise in REST API design and development with FastAPI, Flask, or Django REST Framework

Extensive experience using Jupyter notebooks for data exploration, prototyping, and model training

Proficient with core machine learning libraries such as scikit-learn, TensorFlow, or PyTorch

Demonstrated experience in prompt engineering and working with multiple large language model providers

Solid understanding of vector embeddings, similarity search, and retrieval-augmented generation architectures

Hands-on experience with Docker containerization and orchestration platforms like Kubernetes, Cloud Run, or ECS

Proven track record in productionizing ML/AI systems including logging, tracing, metrics, and automated rollback mechanisms