Translate complex algorithmic ideas - syntax parsing, semantic chunking, custom routing - into reliable, production ready software components.
Build and rigorously evaluate prototypes for future AI initiatives, including open source small language models (SLMs), custom embeddings, and multi agent / cross validation patterns, backed by measurable retrieval and quality metrics.
Architect multi tenant data, identity, and authorization layers that meet enterprise security and compliance requirements.
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Drive proposal and tender quality - Author solution write-ups, bills of materials, and scopes of work that strengthen win rates, including submissions for regulated industries such as oil & gas.
Advise on IT and infrastructure integration - Guide client IT teams through deployment considerations including cloud vs. on-premise architecture, network and connectivity requirements, API integration, and dashboard/digital twin setup.
Build internal technical capability - Develop playbooks, FAQs, and training materials that raise the sales team's technical fluency and reduce dependency on ad hoc engineering support.
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Drive proposal and tender quality - Author solution write-ups, bills of materials, and scopes of work that strengthen win rates, including submissions for regulated industries such as oil & gas.
Advise on IT and infrastructure integration - Guide client IT teams through deployment considerations including cloud vs. on-premise architecture, network and connectivity requirements, API integration, and dashboard/digital twin setup.
Build internal technical capability - Develop playbooks, FAQs, and training materials that raise the sales team's technical fluency and reduce dependency on ad hoc engineering support.
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Partner with AI engineers and developers — including agentic AI initiatives such as multi-agent orchestration systems, natural-language query layers over real-time vision event data, and LLM tool-use and automation — to scope, estimate, and sequence work accurately.
Develop detailed project plans, timelines, and resource allocation; monitor progress and report against KPIs.
Serve as the primary point of contact for clients, managing communication and expectations across technical and business audiences.
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Full Stack EngineeringFrontend: React, Next.js, TypeScript, modern component architectures, state management, real-time and streaming AI interfaces, agent activity and execution interfaces, data visualization.Backend: Node.js, TypeScript, Python, REST APIs, GraphQL, WebSockets and streaming, event-driven architectures, background workers, job queues, distributed systems, authentication and authorization.
Distributed SystemsDesign systems that reliably execute thousands or millions of AI and data-processing tasks. Kubernetes, Docker, Cloud Run and serverless, message queues, Redis, Kafka or equivalent, distributed job processing, concurrency management, rate limiting, retries, idempotency, fault tolerance, observability. You know how to build systems that stay reliable when agents fail, APIs time out, models hallucinate, or downstream services go away.
Data & Learning InfrastructureBuild the infrastructure agents need to learn from historical executions. PostgreSQL, BigQuery or equivalent data warehouses, ClickHouse or analytical databases, vector databases, embeddings, retrieval systems, event logs, feature stores, analytics pipelines, data ingestion.
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