AI Engineer; Junior to Mid-Level

Brief Job Description

We are looking for a driven, detail-oriented AI Engineer to join our growing tech team. In this role, you won't just be copying and pasting API keys; you will actively build, optimize, and deploy machine learning models that solve real-world problems. You will work closely with data scientists, software engineers, and product teams to transition experimental models into scalable, production-ready AI solutions.

If you are passionate about AI, love clean code, and thrive in a collaborative environment, we want to hear from you.

• Preferred Experience Level: Junior to Mid-Level (1 to 3 years of hands‑on experience in AI/ML development).
• Fresh Graduate Suitability: Yes, with a catch. While we prefer candidates with some industry experience, we are absolutely open to exceptional fresh graduates. If you are a recent graduate, you should possess a strong foundational portfolio, relevant internship experience, or significant capstone projects demonstrating hands‑on coding and model‑building capabilities.

• Model Development & Integration: Design, train, test, and deploy machine learning and deep learning models to solve business challenges.
• Data Pipeline
  
  Collaboration:
  
  Work alongside data engineers to clean, preprocess, and structure large datasets for training and evaluation.
• System Optimization: Monitor, maintain, and optimize existing AI models in production to ensure high performance and scalability.
• Cross‑Functional
  
  Collaboration:
  
  Partner with software developers to integrate AI features into our core applications and APIs.
• Stay Curious: Keep up with the latest advancements in AI/ML research and suggest innovative ways to apply them to our products.

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related technical field (or equivalent practical experience).
• Programming: Strong proficiency in Python (familiarity with libraries like Num Py, Pandas, and Scikit‑learn).
• ML Frameworks: Hands‑on experience with at least one major framework, such as Py Torch or Tensor Flow/Keras
  .
• Core CS Concepts: Solid understanding of data structures, algorithms, object‑oriented programming, and software engineering best practices.
• Database Knowledge: Basic proficiency in SQL and experience handling structured and unstructured data.
• Version Control: Comfortable using Git for version control and collaborative development.

While these are not mandatory, having any of the following will definitely make your application stand out from the crowd:

• Cloud Platforms: Knowledge of cloud‑based ML services (e.g., AWS Sage Maker, Google Cloud Vertex AI, or Azure ML). Industry certifications (like AWS Certified Machine Learning – Specialty) are a huge plus.
• MLOps & Deployment: Familiarity with containerization (
  Docker
  ) or model tracking tools (Weights & Biases).
• Generative AI: Experience with Large Language Models (LLMs), prompt engineering, or frameworks like Lang Chain and Llama Index.
• API Development: Experience building APIs to serve models using frameworks like FastAPI or Flask.