- 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.

- Strong
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).

- Strong
understanding of algorithms, data structures, and model evaluation
techniques.