Kuoni Tumlare - Data Engineer - Machine/Deep Learning

Description :

Key Responsibilities :

- Design, develop, and train ML and DL models to solve real-world business problems using structured and unstructured data.

- Implement and optimize AI pipelines from data preprocessing and feature engineering to model training, tuning, and evaluation.

- Experiment rapidly to move from concept POC/MVP/Demoable/Production within defined release cycles.

- Integrate AI solutions into products, services, and APIs, ensuring scalability, efficiency, and maintainability.

- Collaborate with data scientists, software engineers, and product managers to translate AI prototypes into production-grade systems.

- Deploy and monitor models using MLOps pipelines, automating versioning, testing, and retraining.

- Leverage pre-trained models and fine-tune state-of-the-art architectures (e.g , GPT, BERT, LLaMA, CLIP, Whisper) for domain-specific tasks.

- Continuously research emerging AI technologies, frameworks, and open-source tools to enhance solution performance.

- Ensure ethical AI practices and compliance with data privacy regulations throughout the model lifecycle.

- Document processes, results, and model performance clearly for cross-functional visibility and auditability.

Technical Skills :

Programming : Python (required), R, or C++

AI/ML Frameworks: TensorFlow, PyTorch, Keras, Scikit-learn, Hugging Face Transformers

Data Handling: NumPy, pandas, OpenCV, spaCy, NLTK

Generative AI & LLMs: Experience with OpenAI, Anthropic, Meta (LLaMA), Stability AI, or similar APIs/models

MLOps & Deployment: MLflow, Docker, Kubernetes, FastAPI, Flask, TensorRT, TorchServe

Cloud Platforms: AWS SageMaker, Azure ML Studio, GCP Vertex AI

Data Pipelines: Apache Airflow, Kafka, Databricks

Databases: SQL/NoSQL (PostgreSQL, MongoDB, Redis)

Model Monitoring: Prometheus, Grafana, Evidently AI, or custom dashboards

Version Control & CI/CD: Git, GitHub Actions, Jenkins

Additional Tools: LangChain, Vector Databases (Pinecone, Weaviate, FAISS), Streamlit/Gradio for AI demos

Requirements :

- Experience: 36 years of hands-on experience in AI/ML engineering or applied data science.

- Strong foundation in machine learning algorithms, deep learning architectures (CNNs, RNNs, Transformers), and NLP techniques.

- Proven experience in developing and deploying production-level AI/ML models.

- Practical knowledge of software engineering principles, APIs, and system integration.

- Ability to translate complex AI concepts into simple, impactful solutions.

- Familiarity with agile methodologies and iterative release models (monthly demos, bimonthly production).

- Knowledge of AI ethics, fairness, and compliance with global data protection norms.

- Excellent problem-solving skills and curiosity to explore new technologies in the AI space