Senior AI Engineer role at Keyrus focused on delivering AI solutions and leading a team. Engage with clients to translate business needs into AI strategies and solutions.
Responsibilities
Translate business objectives into AI solutions — work directly with clients and stakeholders (executives, managers, and technical teams) to understand challenges, quantify value, and define success metrics before building.
Lead platform and tooling decisions : evaluate and recommend the right approach for each use case, staying true to our vendor-agnostic DNA: AWS, Azure, Salesforce, a custom orchestration stack, and others, based on data gravity, integration, cost, governance, and time-to-value.
Deliver clear trade-off analyses that executives and engineers both understand.
Architect agentic & generative AI systems : design multi-agent workflows, RAG pipelines, tool/function calling, memory and state management, guardrails, and human-in-the-loop fallbacks.
Build, hands-on : develop production-grade agents and automations with LangChain, LangGraph, Bedrock AgentCore, and other tools to integrate with enterprise systems (CRM, ERP) via APIs.
Own quality and reliability : implement evaluation pipelines, observability, prompt/version management; optimize latency, cost, and accuracy of inference.
Deliver PoCs, demos, and production solutions : move ideas from prototype to scaled deployment with proper CI/CD and AI and MLOps practices.
Contribute to pre-sales and solutioning : shape solution offerings, support proposals, and bring market knowledge and technical credibility to client conversations.
Lead and mentor : provide guidance to engineers and interns, define best practices, and help build and scale our AI practice.
Stay ahead : keep pace with a fast-moving field (new models, frameworks, MCP, agent protocols) and bring well-judged ideas to clients and teams.
Requirements
Degree in Computer Science, Engineering, Data Science, or related field, or equivalent professional experience.
5+ years in software/AI/data engineering, including hands-on experience shipping generative or agentic AI to production
Strong Python and solid software-engineering fundamentals (APIs, microservices, version control, testing).
Hands-on experience building AI agents / tool-using LLMs with an orchestration framework.
Practical experience with AWS AI services , especially Amazon Bedrock (foundation models, agents, guardrails) and the broader AWS stack (Lambda, API Gateway, S3, IAM).
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