python developer for AI solutions

DataArt develops AI and machine learning solutions that enhance business capabilities, automate processes, and improve customer and employee experiences.

• 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 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, 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;
• Experience with open-source Agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or the OpenAI Agents SDK;
• Experience with machine learning algorithms, natural language processing, 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.

• No conditions specified