Your Role
As an MLOps Engineer, you’ll bridge the gap between AI research and production. You will design, build, and operate pipelines that bring our AI agents to life. From model training and evaluation to deployment cloud or on prem environment. Your goal: build reliable, explainable, and secure AI agents that perform in real-world, high-impact environments.


Your Responsibilities
- Integrate and optimize open-weight and commercial LLMs (GPT OSS , Mixtral, Command R, OpenAi).
- Design and manage the full model lifecycle (training, tuning, evaluation, deployment).
- Develop and maintain model serving infrastructure for LLMs, ASR, and computer vision.
- Automate workflows for model training, monitoring, and rollout. (CI/CD for AI).
- Implement embeddings, RAG pipelines, and vector-based retrieval systems.
- Develop multimodal processing pipelines (text, audio, image) including OCR, Docling anf Whisper.
- Monitor model performance, latency, and drift; define rollback strategies.
- Define evaluation metrics and human-in-the-loop feedback processes.
- Ensure model governance, access control, and secure data handling.
- Document model architecture, performance benchmarks and deployment procedures.
- Stay ahead of the latest developments in open-source LLMs and responsible AI practices.