At Insignia, we're looking for a Machine Learning Engineer who's built more than just models — someone who's deployed RAG-based solutions across different cloud environments, and knows how to make them scale without breaking.
You don't need to be a generalist — but you should be comfortable jumping between data pipelines, vector stores, and infrastructure quirks depending on the client or project.
What You'll Do:
Design, build, and optimize RAG-based architectures using tools like LangChain, LlamaIndex, and vector databases
Deploy and manage ML systems across AWS, GCP, Azure , or any cloud our clients choose
Improve retrieval quality, reduce latency, and balance cost-efficiency at scale
Collaborate with data scientists, engineers, and product teams to productionize AI features
Write clean, maintainable code — because smart systems only work if they're sustainable
Who You Are:
Strong foundation in Python , ML fundamentals , and data pipelines
Hands-on experience with RAG-based systems and tools like Hugging Face, Pinecone, Weaviate, or FAISS
Comfortable working across multiple cloud platforms and adapting to new infrastructures
Bonus: Background in data engineering , ETL pipelines, or MLOps is highly valued
Curious, collaborative, and excited about real-world AI applications
Why Join Us?
Because great AI isn't built once — it's maintained, optimized, and evolved. If you're ready to build systems that learn, adapt, and keep running — let's talk