Senior Applied AI Engineer

About the role

Digicosolutions is looking for a Senior Applied AI Engineer to build production AI systems for clients. The role is hands-on: you write code, deploy infrastructure and sit in client rooms explaining why the system behaves the way it does. You will design agentic workflows on Claude and AWS Bedrock AgentCore, retrieval systems backed by Bedrock Knowledge Bases, and MCP-based automations that connect directly into client tool stacks such as Notion, Linear, HubSpot, AWS and Google Workspace.

Key responsibilities

• Design and build applied AI systems on Claude and AWS Bedrock AgentCore: RAG pipelines, agentic workflows and multi-tool orchestration via MCP.
• Own delivery of AI components end-to-end: architecture, implementation, evaluation and post-launch monitoring.
• Integrate Claude-based agents with client infrastructure and business tools via MCP servers (HubSpot, Notion, Linear, Google Workspace).
• Turn ambiguous client problems into scoped technical builds with clear success criteria and measurable targets.
• Document architecture and decisions in the client's Notion record.
• Work with Pre-Sales Engineers during scoping calls to validate technical feasibility.
• Select and tune the right model, retrieval strategy and Knowledge Base configuration per use case.
• Build and maintain eval suites (offline test sets, regression checks, LLM-as-judge scoring) to catch quality drift early.
• Design agentic control flow: tool-selection logic, retry/fallback paths and guardrails against runaway loops or unsafe tool calls.
• Instrument production systems with logging, tracing and cost/latency dashboards.
• Write modular infrastructure as code (Terraform) for AI workloads on AWS.
• Review other engineers' AI builds and flag failure modes before they ship.

Required profile

• Master's degree in Computer Science or equivalent.
• Previous experience building and shipping production ML/AI systems.
• Fluent in English and Arabic.

Required skills

• Strong Python engineering and comfort with AWS.
• LLM application patterns: RAG, agentic tool-use via MCP, prompt engineering, evaluation design.
• Hands-on experience with AWS Bedrock AgentCore and Bedrock Knowledge Bases.
• Nice to have: AWS certifications such as Machine Learning Engineer Associate, Generative AI Developer Professional, Solutions Architect Associate, Developer Associate or Security Specialty.