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Job Description
Job Responsibilities
● Architect and oversee the protocol's development, focusing on dynamic node orchestration, layer-wise model sharding, and secure, P2P network communication.
● Drive the end-to-end creation of AI applications, ensuring they are optimised for decentralised deployment and include use cases with autonomous agent workflows.
● Architect AI systems capable of running on decentralised networks, ensuring they balance speed, scalability, and resource usage.
● Design data pipelines and governance strategies for securely handling large-scale, decentralised datasets.
● Implement and refine strategies for swarm intelligence-based task distribution and resource allocation across nodes. Identify and incorporate trends in decentralised AI, such as federated learning and swarm intelligence, relevant to various industry applications.
● Lead cross-functional teams in delivering full-precision computing and building a secure, robust decentralised network.
● Represent the organisation's technical direction, serving as the face of the company at industry events and client meetings.
Requirements :
● Bachelor's/Master's/Ph.D. in Computer Science, AI, or related field.
● 12+ years of experience in AI/ML, with a track record of building distributed systems and AI solutions at scale.
● Strong proficiency in Python, Golang, and machine learning frameworks (e.g., TensorFlow, PyTorch).
● Expertise in decentralised architecture, P2P networking, and heterogeneous computing environments.
● Excellent leadership skills, with experience in cross-functional team management and strategic decision-making.
● Strong communication skills, adept at presenting complex technical solutions to diverse audiences.
Job ID: 107094055
Skills:
.NET, Generative AI / LLMs, LangGraph / Agent Orchestration, Distributed Systems, Software Architecture, System Design, Microservices, Java, Node, AI Agents, Agentic AI, MCP, Event-Driven Architecture, Legacy Application Modernization
Skills:
Data Modeling, ELT, MLops, Etl, LLM ecosystems, Generative AI, Enterprise data platforms, Data engineering fundamentals, AI ML lifecycle, Cloud ecosystems, Enterprise architecture patterns, API integrations, AIOps
Skills:
API design, Distributed Systems, AI Platform architecture, Exposure to cloud ML tooling and MLOps practices, Reliability engineering, Building platform capabilities for AI ML or GenAI solutions
Skills:
Distributed Systems, Devops Tools, Kubernetes, data pipelines, AI evaluation frameworks, vector databases, governance frameworks, prompt engineering, MLOps practices, microservices architecture, enterprise AI platforms, CI CD pipelines, large-scale deployments, Responsible AI, AI ML systems
Skills:
Data Architecture, Encryption, Typescript, Javascript, Python, AWS, Gcp, Data Privacy, Azure, Meta, metadata standards, RAG and knowledge systems, Responsible AI frameworks, Security, bias mitigation, AI reference architectures, cloud-native systems, Google, explainability, workflow orchestration, fairness, taxonomies, Anthropic, secure connectivity, AI architecture, Compliance, semantic caching, CI CD pipelines, end-to-end observability, OpenAI, RAG multi-agent systems