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?
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Spread your wings with clear career paths, technical or leadership.
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Collaborate with 100+ AI experts with 15+ years of applied AI experience, as well as PhD-level researchers with academic backgrounds.
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Tap into domain expertise and knowledge sharing whenever you need it.
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You’re closest to the models and AI itself, building ML/LLM pipelines, integrating, and optimizing models.
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You have a background in deep learning, NLP, or CV, and today you’re hands-on with GenAI and LLMs.
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You know techniques like fine-tuning, prompting, quantization, and LoRA and you understand how models work and how to adapt them for production.
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You’ll dive into the hottest areas of AI: LLMs, agentic frameworks, RAG, inference optimization, and fine-tuning.
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Projects aren’t just PoCs, the models you build go into production and reach real users.
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You won’t be boxed into “just ML” you’ll collaborate with Data Scientists, Software Engineers, and MLOps to deliver end-to-end solutions.
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).
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