AI / ML Engineer

We are hiring a junior applied AI/ML Engineer who is excited to build real-world AI products. You will work on LLMs, data pipelines, and ML models that go into production and power live customer experiences. This role is ideal for someone who wants to grow from hands-on engineering to owning AI systems end-to-end.

Responsibilities:

• Build, test, and deploy AI/ML models into production systems.
• Work with LLMs (ChatGPT-like models), embeddings, and recommendation/search systems.
• Implement data pipelines and model inference services in Python.
• Integrate AI services into web and backend applications.
• Monitor model performance, accuracy, and latency, and continuously improve it.
• Work closely with product, backend, and UI teams to ship AI-powered features.
• Write clean, well-documented, production-ready code.

Requirements:

• 0-2 years of hands-on experience in AI, ML, or data science.
• Strong skills in Python.
• Experience with machine learning or deep learning frameworks (PyTorch, TensorFlow, scikit-learn, etc. ).
• Understanding of ML concepts: training, evaluation, overfitting, embeddings, and inference.
• Exposure to LLMs, APIs, or tools like OpenAI, LangChain, and Hugging Face is a big plus.
• Comfortable working with APIs, JSON, and basic backend concepts.
• Good communication skills - you can explain your work clearly to non-technical teams.
• Curious, a fast learner, and eager to work on real production AI systems.

Good to Have:

• Basic experience with cloud platforms (AWS, GCP, or Azure).
• Familiarity with Docker, Git, or CI/CD.
• Exposure to vector databases, RAG, or prompt engineering.
• Any AI/ML project, internship, GitHub, or portfolio.

Tech Stack (You'll Learn and Work With):

• Python, PyTorch / TensorFlow.
• LLMs, LangChain, embeddings, and vector databases.
• APIs, backend services, and cloud infrastructure.
• Git, Docker, and ML pipelines.