Find your next role

Introduction

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Your role and responsibilities

• Business-to-Technology Bridging

  • Translate domain consulting insights into agent use cases, user journeys, and measurable outcomes.

  • Align stakeholders across product, business, algorithm, and engineering teams; drive decision-making and trade-offs.

• Agent Product & Solution Design

  • Design agent roles, skills/tools, memory and context strategies, orchestration patterns, and human-in-the-loop controls.

  • Define end-to-end workflows integrating LLM reasoning with enterprise tools (APIs, databases, search, optimization solvers).

• Enterprise Architecture Integration

  • Clarify how agents enter the enterprise architecture:

    • Application architecture: interaction patterns, integration styles, service boundaries, APIs, event/batch flows.

    • Data architecture: data sources, knowledge/RAG design, lineage, governance, quality, and evidence/citation strategy.

    • Technology architecture: runtime components, deployment topology, observability, security, and operations model.

  • Produce architecture artifacts (reference architecture, ADRs, interfaces, data contracts, security model).

• Model & Platform Strategy

  • Lead model selection (open/closed, on-prem/cloud, latency/cost/quality trade-offs) and evaluation approach.

  • Define LLMOps/MLOps requirements: prompt/version management, evaluation, monitoring, rollback, and continuous improvement.

• Delivery & Non-Functional Requirements

  • Own NFRs: reliability, performance, scalability, privacy, compliance, auditability, and explainability.

  • Plan compute and capacity (GPU/CPU, concurrency, SLA, throughput), and guide cost/performance optimization.

Required education

Bachelor's Degree

Preferred education

Master's Degree

Required technical and professional expertise

• Experience

  • 5+ years in architecture/technical leadership (software/data/AI), with proven delivery of enterprise systems.

  • Hands-on experience designing and productionizing LLM/agent solutions (RAG, tool-calling, orchestration, guardrails).

• Architecture & Engineering Skills

  • Strong understanding of enterprise application/data/technology architecture and integration patterns.

  • Solid software engineering fundamentals (API design, distributed systems, observability, security-by-design).

  • Familiarity with cloud/on-prem deployments, containers, CI/CD, and operational readiness.

• AI/LLM Competence

  • Model selection and evaluation mindset (quality, safety, latency, cost, governance).

  • Knowledge of RAG and knowledge engineering (retrieval design, grounding, permissions, provenance).

  • Understanding of agent patterns: planning/execution loops, memory/context management, human oversight, and safety controls.

• Non-Functional & Capacity Planning

  • Ability to design SLAs/SLOs and perform performance/capacity sizing (including GPU planning and cost modeling).

• Collaboration

  • Strong communication and stakeholder management; capable of aligning consultants and engineers around a coherent product and architecture plan.

Preferred technical and professional experience

• Experience with agent frameworks (e.g., LangGraph/LangChain) and model serving stacks (e.g., vLLM/TGI).

• Experience in regulated industries (finance, healthcare, public sector) and related compliance/security practices.

• Exposure to optimization/OR or domain decision engines integrated with AI agents.