AI Forward Deployed Engineer

IBM Consulting is focused on long-term client relationships and collaboration across industries, helping clients with their hybrid cloud and AI journeys. As an AI Forward Deployed Engineer, you will design and implement AI solutions tailored to customer needs, integrating AI models with customer data and infrastructure while collaborating with internal teams to enhance AI capabilities.

Responsibilities:

• Design and implement AI models to solve complex business problems, selecting relevant features and algorithms to achieve desired outcomes
• Deliver demos, proofs of concept, and production‑ready implementations while guiding customers on best practices throughout deployment and adoption
• Assess the effectiveness of algorithms using relevant metrics, identifying areas for improvement and optimizing model performance
• Support sales and delivery of services across cloud, data, and AI domains, with a strong understanding of complex, multi-practice engagements
• Articulate integrated solutions that span advisory, engineering, and operational capabilities, leveraging expertise across cloud, data, and AI domains
• Clearly articulate the results of AI initiatives, providing actionable insights and recommendations to drive business outcomes

Requirements:

• 5–10+ years of recent, hands-on experience in data engineering, machine learning, or AI-related roles
• AI/ML expertise: Hands-on experience with AI/ML and Generative AI (LLMs, RAG), including building, fine-tuning, and integrating models into real-world applications
• Agentic AI & workflows: Experience developing or working with agent-based AI solutions (e.g., Amazon Bedrock or similar platforms)
• Software engineering: Strong coding skills (ideally in Python) and experience building production-grade systems on cloud platforms (AWS, GCP, or Azure)
• Data & systems: Working knowledge of SQL, ETL pipelines, and large-scale data processing (e.g., Spark or distributed systems)
• Deployment & tooling: Experience with modern deployment practices (Docker, Kubernetes, CI/CD pipelines)
• Problem-solving: Comfortable navigating ambiguity, prototyping quickly, and turning ideas into working solutions
• Communication: Ability to work with clients and explain technical concepts to non-technical audiences
• Security awareness: Understanding of secure development practices and enterprise considerations for AI systems
• Product mindset: Ability to think end-to-end and design solutions that align with real user needs
• Experience working with senior stakeholders or client leadership
• Background leading or contributing to large-scale AI/ML programs or implementations