Machine Learning Engineer

Job Description :

Key Responsibilities :

- Build and manage MLOps/LLMOps pipelines for deployment, monitoring, evaluation, and lifecycle management.

- Productionize ML models, LLM applications, RAG pipelines, embeddings, and AI agents.

- Automate CI/CD, testing, deployment, retraining, and model/prompt versioning across the engineering stack (Angular, Django, FastAPI/LangGraph agentic service) using Jenkins.

- Implement monitoring and observability for model performance, data quality, drift, reliability, latency, and cost, including Langfuse for tracing and evaluation of the agentic AI platform.

- Build secure, scalable AI infrastructure using AWS (application infrastructure), Hetzner (GPU/model hosting infrastructure), Docker, and Terraform.

- Develop production-grade APIs, model-serving and inference systems, and data/ML pipelines.

- Support self-hosted model infrastructure on Hetzner GPU instances - model serving (e.g., vLLM or TGI for efficient LLM inference) and fine-tuning pipelines (LoRA/QLoRA), including experiment tracking and adapter versioning.

- Partner with data scientists and investment experts to operationalize AI solutions.

Qualifications & Key Skills :

- Bachelor's/Master's in Computer Science/Engineering, Data Science, or a related field.

- 5+ years of experience in MLOps, LLMOps, ML Engineering, DevOps, or Platform Engineering.

- Strong Python and software engineering skills, with CI/CD (Jenkins), cloud (AWS), Docker, and infrastructure-as-code (Terraform).

- Hands-on experience deploying, scaling, and monitoring ML models in production.

- Practical experience with LLMs, RAG, embeddings, vector databases, LLM evaluation, and AI agents.

- Experience with MLflow, Airflow, Kubeflow, or similar MLOps/LLMOps tools.

- Experience deploying and operating web applications and APIs in production (familiarity with Django and FastAPI-based services).

- Experience with GPU infrastructure and self-hosted LLM serving (vLLM, TGI, or similar); exposure to parameter-efficient fine-tuning (LoRA/QLoRA).

- Familiarity with Model Context Protocol (MCP) or similar tool-calling/agent-integration standards.

- Proficiency with Git, APIs, testing, automation, and software development best practices.

- Understanding of AI governance, security, model risk, data privacy, and responsible AI.

- Experience in financial services / investment management will be advantageous.

Skills

Machine Learning, MLOps, LLM, Agentic AI, AWS, Python, GPU, Apache Airflow