AI Engineer with Python

About the Position

We are seeking a skilled AI Engineer to design, develop, and deploy AI and ML driven solutions that enhance business capabilities, automate processes, and improve customer and employee experiences. The ideal candidate has a strong foundation in machine learning, large language models (LLMs), data engineering, and cloud platforms, with the ability to productionize models at scale.

Responsibilities

• Design, build, and deploy machine learning and generative AI models, including LLMs, embeddings, transformers, and RAG pipelines.
• Develop scalable AI services and microservices using Python, REST APIs, and cloud native technologies.
• Optimize models for performance, accuracy, and cost efficiency.
• Work with structured and unstructured datasets for feature engineering, vectorization, and model training.
• Build data pipelines for training, validation, and inference.
• Collaborate with data engineering teams on data ingestion, storage, and governance.
• Implement CI/CD pipelines for machine learning models and MLOps workflows.
• Monitor model performance and drift, and implement retraining strategies.
• Manage model lifecycle processes, logging, and observability.
• Integrate AI systems with enterprise applications, APIs, and cloud platforms such as Azure, AWS, and GCP.
• Build Retrieval Augmented Generation (RAG) architectures leveraging vector databases such as Pinecone, FAISS, Weaviate, or Azure AI Search.
• Ensure solutions align with enterprise security, compliance, and responsible AI standards.
• Work with product, engineering, domain experts, and business teams to translate requirements into technical solutions.
• Communicate AI capabilities and limitations to non technical stakeholders.
• Conduct proofs of concept (POCs), demonstrations, and conceptual solution design activities.

Requirements

• Strong proficiency in Python, including NumPy, Pandas, PyTorch, TensorFlow, and Transformers.
• Hands on experience with LLMs, including OpenAI, Azure OpenAI, Anthropic, and Llama models.
• Experience with machine learning algorithms, natural language processing (NLP), deep learning, and vector embeddings.
• Experience with cloud platforms such as Azure, AWS, and GCP, including serverless computing services.
• Familiarity with MLOps tools such as MLflow, Kubeflow, Azure Machine Learning, Amazon SageMaker, or Databricks.
• Experience working with vector databases, including Pinecone, Chroma, FAISS, and Azure AI Search.
• Knowledge of containerization and orchestration technologies such as Docker and Kubernetes.