Senior Python Engineer with AI Exposure

## This is how we work - you develop several projects simultaneously - you have influence on the technological solutions applied - you have influence on the product - you focus on product development - agile ## Team members - backend developer - technical leader - devOps - data scientist - product owner - project manager ## 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. - 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. ## 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. - Our client is a UK financial services group operating in a highly regulated environment, currently rebuilding its data and analytics capability as part of a wider platform modernisation programme.