AI/ML Engineer

Requirements

About the Role:

AI/ML Engineer

Key Responsibilities:

• Design and implement scalable ML solutions for classification, regression, clustering, and recommendation use cases
• Collaborate with data scientists, engineers, and product teams to translate business requirements into ML use cases
• Preprocess large datasets using Python, SQL, and modern ETL tools
• Train, validate, and optimize machine learning and deep learning models
• Deploy models using MLOps best practices (CI/CD, model monitoring, versioning)
• Continuously improve model performance and integrate feedback loops
• Research and experiment with the latest in AI/ML trends, including GenAI, LLMs, and transformers
• Document models and solutions for reproducibility and compliance
  

Required Skills:

• Strong proficiency in Python, with hands-on experience in NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, etc.
• Solid understanding of supervised and unsupervised learning, NLP, and time-series forecasting
• Experience with cloud platforms such as AWS, GCP, or Azure (preferred: SageMaker, Vertex AI, or Azure ML Studio)
• Familiarity with Docker, Kubernetes, and MLOps practices
• Proficient in writing efficient and production-grade code
• Excellent problem-solving and critical-thinking skills
  

Good to Have:

• Experience with LLMs, Generative AI, or OpenAI APIs
• Exposure to big data frameworks like Spark or Hadoop
• Knowledge of feature stores, data versioning tools (like DVC or MLflow)
• Published work, research papers, or contributions to open-source ML projects