Artificial Intelligence/Machine Learning Engineer - LLM Models

Description :

Primary Responsibilities :

Research and Development :

- Lead applied research in LLMs, generative AI, and multimodal models

- Evaluate and experiment with state-of-the-art architectures (e.g., Transformers, Diffusion Models, Retrieval-Augmented Generation)

- Publish internal whitepapers and contribute to external conferences where applicable.

Engineering and Implementation :

- Design and implement scalable AIML pipelines using frameworks like PyTorch, TensorFlow, Hugging Face, and MLflow

- Collaborate with engineering teams to deploy models into production using MLOps best practices (CI/CD, model versioning, monitoring)

Tooling and Infrastructure :

- Evaluate and integrate advanced AIML tools such as GitHub Copilot, Windsurf, Vertex AI, Azure OpenAI, and Hugging Face Transformers

- Work with cloud platforms (AWS, Azure, GCP) to ensure scalable and secure model deployment

Architecture and Strategy :

- Architect end-to-end AIML systems including data ingestion, model training, inference, and feedback loops

- Partner with other architects and product leaders to align AIML capabilities with business goals 3

Mentorship and Collaboration :

- Mentor junior engineers and researchers

- Collaborate with cross-functional teams including data scientists, software engineers, and business stakeholders

Required Qualifications :

- Master's or relevant in Computer Science, Machine Learning, or related field

- 8+ years of experience in AIML engineering and research

- Hands-on experience with MLOps, model deployment, and monitoring

- Experience with experiment tracking tools (e.g., Weights & Biases, MLflow)

- Familiarity with AIML governance, ethics, and responsible AI practices

- Proven expertise in LLMs, generative AI, and deep learning

- Proven solid programming skills in Python and familiarity with ML libraries (e.g., scikit-learn, Keras)