Ai-ml engineer

Title: AI/ML Engineer.

Primary Duties: Model Development and Deployment.

Role Purpose

Responsible for designing, training, validating, and deploying the AI/ML models using ML frameworks such as Tensor Flow or Py Torch and Cloud platform such as AWS, Microsoft Azure and Google Cloud. Ensures high model accuracy, scalability, and seamless integration with DS365.ai and the REST API infrastructure.

Key Responsibilities

Develop and fine-tune Supervised Learning (Regression and Classification), Unsupervised Learning (K-Means, Clustering), Deep Learning (CNN, LSTM, RNN), Reinforcement Learning (Q Learning models, DQN) and Generative Models (GANs) using Tensor Flow or equivalent frameworks.

Prepare, preprocess, and analyze training datasets, apply balancing and augmentation techniques.

Evaluate model performance using metrics such as accuracy, precision, recall, and F1-score.

Deploy trained models as REST API services with version control and monitoring.

Collaborate with data engineers and domain SMEs to validate model predictions and interpret results.

Support retraining cycles and document model training pipelines.

Required Skills & Experience

Bachelor’s or master’s degree in computer science, data science, or a related field.

4 to 7 years of experience in AI/ML model development and deployment.

Strong proficiency in Tensor Flow, Py Torch, Keras, Python, and REST API development.

Strong proficiency in training dataset preparation and labeling tools such as Label Studio

Experience with AWS services, Google Cloud and Microsoft Azure (S3, Google Cloud Storage (GCS), Azure Blob Storage, Lambda, Sage Maker, Vertex AI, Azure Machine Learning, ).

Solid understanding of data labeling, preprocessing, and feature engineering.

Familiarity with MLOps practices, model versioning, and reproducibility.

Exposure or experience to Oil & Gas data is a strong advantage.

Soft Skills

Analytical mindset.

Problem-solving ability.

Documentation discipline.

Effective communication with both technical and non-technical stakeholders.