Capgemini - Machine Learning Engineer - Tensorflow/PyTorch

Description :

Technical Expertise :

- Solid hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, and Hugging Face building deep learning & machine learning models.

- Proficient in fine-tuning LLMs (Large Language Models) using LoRA, QLoRA, and PEFT techniques to optimize resource efficiency.

- Strong knowledge of Graph Databases (e.g., Neo4j, TigerGraph) and their integration with machine learning systems.

- Experience working with Vector Databases (e.g., Pinecone, Weaviate, Milvus, FAISS or ChromaDB).

- In-depth understanding of neural network architectures, including Transformers, GANs, and Diffusion Models.

- Solid expertise in NLP, retrieval systems, and multimodal AI integration (text, images, audio).

- Proficiency in Python and ML libraries (NumPy, Pandas, Scikit-learn).

- Experience with deploying machine learning models on cloud platforms like AWS, GCP, or Azure.

Soft Skills :

- Strong analytical and problem-solving abilities to tackle complex AI challenges.

- Excellent communication skills, with the ability to explain technical concepts to both technical and non-technical stakeholders.

- Ability to mentor junior developers and collaborate effectively within a cross-functional team.

- Self-driven with the ability to work independently on projects and tasks, while aligning with team goals.

Education and Experience :

- Bachelor's or Masters degree in Computer Science, Data Science, Artificial Intelligence, or related fields.

- 2-5 years of experience in machine learning, generative AI, NLP, or graph-based AI solutions.

Preferred Skills (Nice-to-Have) :

- Familiarity with multimodal AI techniques (combining text, images, and other data types).

- Knowledge of MLOps practices, including tools for continuous deployment and monitoring of AI models.

- Published research or open-source contributions in generative AI or graph-based AI.

- Awareness of ethical AI principles and data privacy regulations.