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Showing 2 of 3302 jobs

Senior Python Backend Engineer, Reef Technologies

Company:
Location: Remote
Published: 1970-01-01

Warsaw (fully remote), Poland Senior Python Backend Engineer We’re looking for Python backend engineers to work on a trustless supercluster of performance-proofed GPU-enabled sandboxed docker container runners controlled by truly decentralized algorithms (not just PAXOS or RAFT). Wow, that was a mouthful... If you found it interesting, join Reef Technologies and tackle complex technical challenges like this on your own terms: Contribute from wherever you like—we are fully remote but like to stay in sync, so we’re currently hiring in EMEA/APAC. Set your own time commitment, as long as it’s at least 30h per week See how we work in our handbook Influence the way we operate through our Sociocracy 3.0 decision-making process Salary on a B2B contract: 45-70 USD or 180-280 PLN per hour Flexible work schedule (as measured with a time tracker) We automatically adjust rates based on inflation twice a year So, what do you think? You don't even need a CV, just click here and shoot a few quick commands to apply. We're looking for someone with 5+ years of programming experience, including at least a year with Python (including opensource and significant personal projects).
GenAI & Python Specialist, Deloitte

Company:
Location: Remote
Published: 1970-01-01

Toronto, Ontario, Canada Deloitte is seeking an experienced GenAI & Python Specialist to join our dynamic Operate team on a 1‑year fixed term employment basis. In this role, you will design, build, and scale Generative AI solutions using Python‑based frameworks, Large Language Models (LLMs), and advanced retrieval‑augmented generation (RAG) techniques. What will your typical day look like? -Strong Python development for GenAI and agentic systems Design and implementation of Agentic AI workflows using LangChain, LangGraph, ADK, MCP Development and orchestration of multi‑agent systems Implementation of Retrieval‑Augmented Generation (RAG), including advanced RAG techniques Integration and management of vector databases for RAG workflows Design and implementation of memory systems (short‑term, long‑term, persistence, conversational checkpoints) Agentic AI design and orchestration across workflows Work with AI/GenAI models including Gemini and Claude 4.5 on Vertex AI Development of business co‑pilots, including co‑pilot solutions for Risk Analysts Support for business process optimization using GenAI Use of NLU as an add‑on capability where requiredContribution to CTO‑led tools, including: Agent Flow for observability, drift monitoring, feedback loops ML Flow for model evaluation and drift detection Sourcing test results management, including truth tables and evaluations Bringing Agent Flow into existing projects Domain‑specific GenAI solutions for DRO (Data and Regulatory Operations): QA validation and KYC document processing Systematic capture and validation of required fields Automated document checking without extraction Record retrieval and comparison (DNC, ANC, SMC) Agentic framework for document management, data analysis, and data engineering Co‑Labs initiatives: Consolidation across MCP endpoints Agent registries consolidation Combining assets into an accessible catalog Python‑based web scraping and utility development Work with existing Python GitHub repositories Integration with case management tools and Appian workflows (NA10) Building agents on top of Appian workflows using ADK You are someone with these skills, experience & qualifications: 6+ years of hands‑on experience designing and implementing AI / GenAI solutions using Python Strong proficiency in Python engineering for data processing, model integration, APIs, and microservices Deep understanding of Generative AI, LLMs, and NLP concepts and architectures Experience building agentic AI workflows using LangGraph, including multi‑step reasoning, tool‑calling, and planner–executor patterns Advanced knowledge of RAG techniques, including hybrid search, vector and multi‑vector retrieval, embeddings, and context optimization Experience designing and deploying APIs and microservices to production environments Familiarity with traditional ML/NLP techniques such as clustering, extraction, and enrichment to complement GenAI solutions Strong communication skills, a collaborative mindset, and a passion for continuous learning and innovation in the GenAI space
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