Machine Learning Engineer

AI/ML Engineer

Key Responsibilities:

Model Development & Implementation

• Design, train, and optimize

  machine learning

  and

  deep learning

  models for predictive analytics, natural language processing, computer vision, or recommendation systems.
• Perform

  feature engineering

  , data preprocessing, and exploratory data analysis (EDA).
• Evaluate models using appropriate metrics and validation techniques.
• Implement scalable ML pipelines and experiment tracking.

Deployment & MLOps

• Build and maintain

  end-to-end ML pipelines

  , from data ingestion to model serving.
• Deploy models into production using tools like

  Docker, Kubernetes, TensorFlow Serving, TorchServe, or MLflow

  .
• Monitor model performance, perform drift detection, and retrain models as needed.
• Collaborate with DevOps to ensure CI/CD for ML workflows.

Collaboration & Research

• Partner with data scientists, software engineers, and product managers to identify opportunities for AI integration.
• Stay up to date with emerging trends in

  AI, ML, and GenAI

  technologies.
• Contribute to technical documentation, code reviews, and internal knowledge sharing.

Data Management:

• Work closely with

  data engineering teams

  to ensure clean, structured, and accessible datasets.
• Apply techniques for

  data augmentation

  ,

  dimensionality reduction

  , and

  data quality improvement

  .

Required Skills and Qualifications:

• Bachelor's or Master's degree in

  Computer Science, Artificial Intelligence, Data Science, Statistics, or a related field

  .

• 25+ years

  of experience (adjust based on role level) in building and deploying ML models in production.
• Proficiency in

  Python

  and ML/DL frameworks such as

  TensorFlow, PyTorch, scikit-learn, XGBoost, Hugging Face Transformers

  , etc.
• Strong understanding of

  machine learning algorithms

  ,

  deep learning architectures (CNNs, RNNs, LSTMs, Transformers)

  , and

  statistical modeling

  .
• Experience with

  SQL, Pandas, NumPy

  , and

  data visualization tools

  (Matplotlib, Seaborn, Plotly).
• Knowledge of

  MLOps frameworks

  (MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, etc.).
• Familiarity with

  cloud platforms

  (AWS, GCP, Azure).
• Excellent problem-solving, analytical, and communication skills.

Preferred Qualifications:

• Experience with

  large language models (LLMs)

  and

  generative AI (GenAI)

  .
• Exposure to

  vector databases

  (Pinecone, FAISS, Milvus) and

  retrieval-augmented generation (RAG)

  systems.
• Background in

  data engineering

  (ETL, data pipelines) or

  software engineering

  (API development).
• Publications or contributions to open-source AI/ML projects.

Soft Skills:

• Strong analytical mindset and curiosity for data-driven insights.
• Ability to translate complex technical concepts into actionable business outcomes.
• Collaboration and teamwork across multi-disciplinary teams.
• Ownership mentality and accountability for deliverables.

Location

Experience

Salary