Senior Python Engineer with AI Exposure

Our 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 AWS Bedrock/AgentCore, Google Vertex AI, Azure AI Foundry, or other Agentic AI platforms, as well as open-source Agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or the OpenAI Agents SDK.
• 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.

Optional

• Hands on experience with AWS AgentCore Gateway A2A enforcement models.
• Experience with AWS AgentCore Runtime multi agent invocation patterns.
• Experience defining enterprise standards and governance frameworks for agent to agent communication.
• Knowledge of LangGraph or CrewAI multi agent orchestration frameworks.
• Experience with platform observability, monitoring, and operational analytics.
• Understanding of AI platform governance, security controls, and compliance requirements.

Your 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.

This is how we organize our work

This is how we work on a project

• code review
• Continuous Deployment
• Continuous Integration
• DevOps
• documentation
• integration tests
• performance tests
• test automation

DataArt Poland sp. z o.o.