Senior Machine Learning Engineer (LLMs)

At deepsense.ai, you won’t just build AI solutions – you’ll shape how companies around the world use them.

By joining us, you’ll:

Work with partners like OpenAI, NVIDIA, Anyscale, LangChain, Crusoe, and ElevenLabs.

Explore and apply the newest tech: LLMs & RAG, MLOps, Edge Solutions, Computer Vision, Predictive Analytics.

Tackle challenges in software & tech, pharma & healthcare, manufacturing, retail, telecoms & media.

Contribute to open-source projects – just take a look at our latest solution,  ragbits , an agentic RAG framework with over 1.6k stars on GitHub.

And the best part of working at deepsense.ai?

Spread your wings with clear career paths, technical or leadership.

Collaborate with 100+ AI experts with 15+ years of applied AI experience, as well as PhD-level researchers with academic backgrounds.

Tap into domain expertise and knowledge sharing whenever you need it.

Daily tasks

• You’re closest to the models and AI itself, building ML/LLM pipelines, integrating, and optimizing models.
• You have a background in deep learning, NLP, or CV, and today you’re hands-on with GenAI and LLMs.
• You know techniques like fine-tuning, prompting, quantization, and LoRA and you understand how models work and how to adapt them for production.
• You’ll dive into the hottest areas of AI: LLMs, agentic frameworks, RAG, inference optimization, and fine-tuning.
• Projects aren’t just PoCs, the models you build go into production and reach real users.
• You won’t be boxed into “just ML” you’ll collaborate with Data Scientists, Software Engineers, and MLOps to deliver end-to-end solutions.

Requirements

The ideal candidate:

Has 6 + years of experience in ML engineering and working with models in production environments .

Brings hands-on expertise with Large Language Models (LLMs) and Generative AI , including integration and inference optimization (latency, cost, scalability).

Is familiar with frameworks and tools for building and orchestrating LLM pipelines (LangChain, LlamaIndex, RAG, agent frameworks).

Can design and implement end-to-end ML/LLM pipelines , from data preparation and training/fine-tuning to production-grade APIs.

Has experience with cloud platforms (AWS, GCP, Azure) and their AI/ML services (e.g., SageMaker, Vertex AI, Azure ML).

Has worked with SQL, NoSQL , and vector databases (Pinecone, FAISS, Weaviate).

Is fluent in Python and experienced with ML frameworks (PyTorch, TensorFlow, Hugging Face).

Knows how to deploy and monitor models (MLOps: CI/CD for models, logging, observability, quality monitoring).

Communicates clearly and can collaborate effectively with both Data Scientists and product/client teams.

Bonus : experience in prompt engineering and building simple AI user interfaces (Streamlit, Gradio).

Must have: Python, Machine learning, Docker, Kubernetes, GenAI, LLM, AI, Cloud platform, Azure, Azure ML, SQL, PyTorch, TensorFlow

Nice to have: NoSQL, GCP, User Interface