Summary
We are seeking a skilled AI/ML Engineer to join our IT Services team. In this pivotal role, the successful candidate will design, develop, and deploy advanced machine learning models and artificial intelligence solutions. This position is essential for driving innovation and delivering high-impact technical outcomes that leverage data science to solve complex business challenges.

Responsibilities

Architect and implement scalable machine learning pipelines using tools like Pandas, NumPy, Spark, and SQL.
Containerize applications and manage orchestration environments with Docker and Kubernetes while maintaining version control via Git and CI/CD workflows.
Develop and integrate RESTful APIs to support microservices architectures.
Build and optimize vector search systems utilizing platforms such as Pinecone, Weaviase, and FAISS.
Establish robust model monitoring protocols and automate the retraining processes to ensure system reliability.
Leverage generative AI frameworks including Hugging Face, LangChain, LLMs, and RAG architectures.
Deploy and manage models on cloud platforms including Azure ML, Azure OpenAI, AWS SageMaker, and GCP Vertex AI.
Implement MLOps best practices using MLflow and Kubeflow to streamline the machine learning lifecycle.

Requirements

Requirements:

Possess 4 to 6 years of professional experience in data engineering or machine learning roles.
Demonstrate proficiency with data manipulation libraries such as Pandas and NumPy, alongside big data processing with Spark and SQL.
Have hands-on experience with containerization, orchestration, and DevOps tools like Docker, Kubernetes, Git, and CI/CD.
Show expertise in designing REST APIs and working within microservices ecosystems.
Understand vector database technologies and similarity search mechanisms using Pinecone, Weaviase, or FAISS.
Be capable of setting up automated model monitoring and retraining workflows.
Possess strong knowledge of generative AI tools, specifically Hugging Face, LangChain, Large Language Models, and Retrieval-Augmented Generation.
Have practical experience deploying models on major cloud providers, including Azure, AWS, or GCP.
Be familiar with MLOps frameworks such as MLflow and Kubeflow.

Erforderliche Kompetenzen

PandasNumPyApache SparkSQLDocker (containerization)Kubernetes (orchestration)Git (version control)CI/CD pipelinesRESTful API designMicroservices architectureVector search systems (Pinecone, Weaviase, FAISS)Model monitoring and automated retrainingGenerative AI frameworks (Hugging Face, LangChain, LLMs, RAG)Cloud ML deployment (Azure ML, Azure OpenAI, AWS SageMaker, GCP Vertex AI)MLOps frameworks (MLflow, Kubeflow)

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