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Capgemini
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Machine Learning Engineer

Machine Learning Engineer

Capgemini

6 - 8 years

8 - 17 Lacs

bengaluru

Posted:1 day ago| Platform:

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Skills Required

opencv natural language processing aiml neural networks machine learning artificial intelligence image processing computer vision ml

Work Mode

Hybrid

Job Type

Full Time

Job Description

Capgemini is Hiring on Below Requirment.

Job Title: Machine Learning Engineer

Location:Bangalore

Experience:6-9 years

Qualifications and Skills:

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.

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