Python Engineering & System Development :

- Develop robust, scalable AI/ML applications using Python.

- Build modular, production-ready code following strong software engineering practices (testing, linting, packaging, version control).

- Develop and maintain APIs using FastAPI, Flask, or similar frameworks.

- Debug and optimize deployed systems across backend services, APIs, frontend integrations, and cloud dependencies.

AI/ML & GenAI Development :

- Lead data discovery, feature engineering, experimentation, and model development.

- Work across classical ML, deep learning, and NLP/LLM-based systems (embeddings, fine-tuning, RAG).

- Design rigorous evaluation strategies including offline metrics, regression testing, failure-mode analysis, and human-in-the-loop validation for GenAI systems.

- Ensure model reproducibility, documentation, versioning, and traceability.

Deployment Pipelines & DevOps :

- Build and manage CI/CD pipelines for ML and AI workflows.

- Containerize applications using Docker and deploy via Kubernetes (preferred).

- Implement DevOps, MLOps, and LLMOps best practices.

- Manage release cycles, change controls, and environment configurations.

- Ensure secure API integrations and scalable inference pipelines.

Cloud & Client Environment Deployment :

- Deploy AI/ML solutions on Microsoft Azure (Function Apps, scaling, integrations).

- Manage configuration, monitoring, and performance optimization in live environments.

- Support deployments within client-controlled infrastructures.

- Work within client security, access control, and infrastructure constraints.

- Own end-to-end troubleshooting and incident management in production systems.

Technical Requirements :

- Strong proficiency in Python and ML/DS libraries (Pandas, scikit-learn, PyTorch/TensorFlow, XGBoost/LightGBM).

- Strong SQL and data wrangling skills; experience with Spark/Databricks is a plus.

- Hands-on experience with API-based deployments and cloud-native architectures.

- Experience with CI/CD tools, containerization, and orchestration frameworks.

- Familiarity with LLM orchestration tools (Langgraph, Langflow, Flowise) is a plus.

Soft Skills :

- Strong cross-functional collaboration skills.

- Ability to communicate technical solutions clearly to both technical and non-technical stakeholders.

- High ownership mindset with full lifecycle accountability.