AI ML Engineer

Role & responsibilities

• Design and develop machine learning models:

  Building and optimizing ML algorithms for specific business needs.

• Data analysis and preprocessing:

  Working closely with large datasets to extract insights, ensure quality, and prepare data for modeling.

• Collaboration:

  Partnering with data scientists, software engineers, product managers, and stakeholders to define project requirements and implement solutions.

• Deploying and integrating AI solutions:

  Implementing and maintaining production-ready AI models and integrating them into business systems.

• Staying current:

  Keeping up with the latest advancements in AI/ML technologies and frameworks.

• Documentation:

  Clearly documenting processes, architectures, and best practices for future reference.

Preferred candidate profile

• Education:

  Bachelors or Master’s degree in Computer Science, Data Science, Engineering, or a related field.

• Programming Skills:

  Proficiency in Python is most common, with experience in Java or C++ as valuable additions.

• ML Frameworks:

  Hands-on work with TensorFlow, PyTorch, Keras, Scikit-learn, and often LangChain or similar advanced AI libraries.

• Data Skills:

  Ability to process and analyze large datasets using tools like Pandas, SQL, Spark, and Hadoop.

• Math Foundations:

  Strong understanding of linear algebra, calculus, probability, statistics, and optimization algorithms.

• Cloud & MLOps:

  Experience deploying models on AWS, GCP, or Azure and knowledge of MLOps tools and best practices is a significant plus.

• Specialized Tech:

  Familiarity with vector databases, LLMs (Large Language Models), Retrieval-Augmented Generation (RAG), or technologies like Docker/Kubernetes is often preferred for advanced roles.