AI Engineer (GenAI & Integration)

Description abra professional services is seeking for an AI Engineer (GenAI & Integration) The AI Engineer (GenAI & Integration) designs, builds, and deploys AI-powered solutions within enterprise systems and business workflows. The role focuses on practical application of AI by integrating models, data, and tools into real operational environments to enable automation, decision support, and productivity gains. The role emphasizes implementation and integration rather than model development, bridging AI capabilities with enterprise systems and workflows. Key Responsibilities Design and implement end-to-end AI solutions aligned with business needs Integrate AI capabilities with enterprise systems, APIs, and data platforms, including legacy environments Develop AI agents and automation workflows for multi-step task execution Build AI-driven applications, including copilots and decision-support tools Develop and maintain integration layers, including MCP-based and similar AI integration services Evaluate, test, and validate AI outputs to ensure accuracy, reliability, and quality in production Deploy, monitor, and optimize AI solutions in production environments Maintain clear and structured documentation across solutions, integrations, and processes Lead implementation of AI solutions in collaboration with business, IT, and engineering teams to ensure scalable and compliant delivery Requirements Required Skills 3+ years of hands-on software development experience, preferably in Python/backend or integration-focused roles, including practical experience with GenAI technologies such as LLMs, RAG, AI agents, and automation workflows. Proven ability to independently design, build, and deploy end-to-end production-grade solutions within complex enterprise environments. Strong programming skills (Python preferred) Hands-on experience with LLMs, prompt design, and RAG solutions Experience integrating AI systems with enterprise data, APIs, and services (e.g., MCP or similar) Experience building AI agents and automation workflows Proven ability to build and deliver production-grade systems end-to-end Understanding of system design, deployment, and production environments Experience validating and evaluating AI system outputs for quality and reliability Basic project management capabilities to coordinate tasks, timelines, and cross-functional collaboration Preferred Qualifications Experience with enterprise platforms (e.g., ERP, CRM, M365) Documentation skills for technical solutions, workflows, and integration processes Exposure to cloud environments and AI/LLM orchestration frameworks Understanding of secure development practices and AI governance considerations Success Criteria Delivery of production-ready AI solutions Measurable impact on business processes and efficiency Adoption and effective use of AI across teams Reliability and stability of deployed AI systems in production