Work
with LLMs, NLP models, neural networks, and other advanced AI
techniques.

Build
scalable data pipelines and automate model training, validation, and
deployment workflows.

Optimize
model performance, latency, and accuracy using contemporary techniques.

Work
closely with cross -functional teams including Data Engineering, Product,
and Software Engineering.

Perform
exploratory data analysis, feature engineering, and experiment tracking.

Integrate
AI models into applications using APIs, microservices, or cloud -based
solutions.

Stay updated with the latest advancements in AI,
ML, and GenAI technologies.


Requirements
10+ years of hands -on experience in AI/ML engineering.

Solid
proficiency in Python, ML libraries (NumPy, Pandas, Scikit -learn),




and DL frameworks (TensorFlow/PyTorch).

Experience
with LLMs, NLP, transformers, embeddings, and prompt optimization.

Knowledge
of MLOps tools like MLflow, Kubeflow, Airflow, or similar.

Experience
in using cloud platforms (AWS / Azure / GCP) for AI model
deployment.

Hands -on
experience with vector databases, feature stores, or model
registries (nice to have).

Familiarity
with API development, microservices architecture, and
containerization (Docker, Kubernetes).

Robust
understanding of algorithms, data structures, and model evaluation
techniques.