We’re looking for a Machine Learning Engineer to join a fast-growing global product team, working on cutting-edge Generative AI and scalable ML systems. If you’re passionate about building impactful AI solutions and working with modern ML infrastructure, this role is for you.

What You’ll Do:

Design and develop robust, production-ready ML architectures with end-to-end ownership — from data pipelines to real-time inference.

Build and fine-tune models using LLMs, RAG (Retrieval-Augmented Generation), and other GenAI techniques.

Work with vector databases like FAISS, Pinecone, or Weaviate to build intelligent retrieval systems.

Deploy models using cloud-native tools (AWS preferably), Docker, Kubernetes, and MLOps best practices.

Collaborate closely with product, backend, and data teams to align solutions with real-world user needs.

Mentor junior engineers and contribute to scaling the team’s technical capabilities.

What We’re Looking For:

8–12 years of experience in machine learning engineering or related software roles.

Strong expertise in Python with deep knowledge of TensorFlow and PyTorch.

Proven experience in LLMs, RAG systems, and Generative AI frameworks (e.g., LangChain).

Hands-on knowledge of vector search, MLOps pipelines, and model monitoring/tuning.

Experience deploying ML solutions in cloud environments (AWS preferred)

Solid grasp of data structures, algorithms, and distributed systems.

If this role is in line with your profile, write to me at [email protected] and let's connect!