Machine Learning Engineer

Machine Learning Engineer

Key Responsibilities:

• Design, build, and optimize

  machine learning and generative AI models

  , including model fine-tuning, embedding generation, and evaluation.
• Develop and maintain

  MLOps pipelines

  for model deployment, monitoring, retraining, and scaling across production environments.
• Implement

  data preprocessing, feature extraction, labeling, and augmentation

  workflows for robust model performance.
• Deploy and manage models in

  containerized environments

  using

  Docker

  and

  Kubernetes

  , integrating with

  cloud AI services

  (AWS, Azure, or GCP).
• Collaborate with cross-functional teams to identify, prototype, and productionize

  AI-driven solutions

  aligned with business objectives.
• Conduct

  A/B testing

  , monitor model drift and performance, and iterate model improvements.
• Implement

  data versioning

  ,

  feature store management

  , and

  experiment tracking

  to ensure reproducibility.
• Maintain comprehensive documentation for algorithms, data pipelines, and model lifecycle management.
• Stay current with advancements in

  machine learning, generative AI

  , and

  MLOps practices

  .

Skills and Tools Required:

• Solid foundation in

  Python

  ,

  NumPy

  ,

  Pandas

  ,

  Scikit-learn

  , and

  Matplotlib

  for data analysis and model development.
• Strong hands-on experience with

  TensorFlow

  or

  PyTorch

  for ML/DL model development.
• Understanding of

  AI model serving

  ,

  API exposure

  , and

  inference optimization

  for real-time predictions.
• Familiarity with

  MLOps principles

  and tools such as

  MLflow

  ,

  Kubeflow

  , and

  DVC

  for model orchestration and version control.
• Exposure to

  Generative AI

  techniques, including

  transformer architectures

  ,

  GANs

  ,

  VAEs

  , and

  fine-tuning

  for domain-specific tasks.
• Basic understanding of

  RAG (Retrieval-Augmented Generation) pipelines

  ,

  vector databases

  , and

  similarity search mechanisms

  .
• Experience in

  A/B testing

  ,

  model monitoring

  , and

  cloud-based ML operations

  .
• Knowledge of

  data versioning

  and

  feature store tools

  such as

  Feast

  or

  Tecton

  .
• Familiarity with

  version control systems

  like

  Git

  and CI/CD practices for ML workflows.

Tools & Technologies:

• Machine Learning Frameworks:

  TensorFlow, PyTorch, Keras

• Data Processing Libraries:

  Pandas, NumPy, Scikit-learn

• Visualization Tools:

  Matplotlib, Seaborn

• MLOps Tools:

  MLflow, Kubeflow, DVC, Airflow

• Feature Stores & Data Versioning:

  Feast, Tecton

• Containerization & Orchestration:

  Docker, Kubernetes

• CI/CD & Automation:

  Jenkins, GitHub Actions

• Cloud Platforms:

  AWS, Azure, or GCP (AI/ML Services)

• Vector Databases:

  FAISS, Pinecone, Chroma (for embedding-based search)

Preferred Qualifications:

• Experience working on projects involving Generative AI applications.
• Familiarity with containerization technologies like Docker and orchestration tools such as Kubernetes.
• Understanding of data privacy and ethical considerations in AI and machine learning.

If you are passionate about leveraging machine learning to create innovative solutions and have a keen interest in Generative AI and MLOps, we encourage you to apply and join our forward-thinking team.

Mandatory Skills

AI ML Engineer, MLOps, Kubernetes