Responsibilities
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.
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).
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.
Architect end-to-end AIML systems including data ingestion, model training, inference, and feedback loops.
Mentor junior engineers and researchers.




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

Qualifications
Undergraduate degree or equivalent experience.
Bachelor's or relevant in Computer Science, Machine Learning, or related field.
4+ years of experience in AIML engineering and research.
Proven expertise in LLMs, generative AI, and deep learning.
Robust programming skills in Python and familiarity with ML libraries (e.g., scikit-learn, Keras).
Experience with experiment tracking tools (e.g., Weights & Biases, MLflow).
Hands-on experience with MLOps, model deployment, and monitoring.
Familiarity with AIML governance, ethics, and responsible AI practices.

📌 Ai/ml Engineer Bengaluru
🏢 Optum
📍 Bengaluru