Forward Deployed Engineer – AI Strategist

Position- Forward Deployed Engineer AI Strategist

Location- Dallas, TX

Duration - 12 months

We are seeking a highly experienced Forward Deployed Engineer - AI Strategist to lead the endto-end adoption, deployment, and scaling of AI solutions across enterprise environments. This role combines AI strategy, solution architecture, and deployment leadership, working closely with business stakeholders to drive measurable impact across domains such as manufacturing, supply chain, and operations. You will act as the technical owner and strategic advisor, ensuring AI initiatives align with business objectives and deliver tangible ROI. Key Responsibilities 1. AI Strategy & Business Alignment Define and execute the enterprise AI roadmap aligned with organizational goals. Identify high-impact use cases across manufacturing, supply chain, agriculture, and commercial operations. Evaluate opportunities for process optimization, automation, and decision intelligence using AI. Drive adoption of modern AI technologies including LLMs, AI agents, and advanced analytics. 2. AI Solution Architecture Architect end-to-end AI systems, including: o Data pipelines o Feature/embedding pipelines o LLM integrations and AI agents o Real-time inference services Design scalable systems using cloud platforms (Azure preferred, AWS/GCP). Make trade-offs for latency, scalability, cost, and security. Define microservices architecture using Docker & Kubernetes. 3. Forward Deployment & Delivery Lead deployment of AI solutions onsite or remotely as the technical owner. Drive POCs, pilot programs, and MVP-to-production transitions. Collaborate with DevOps, security, and enterprise IT teams for seamless integration. Conduct demos, training sessions, and knowledge transfer workshops. 4. Stakeholder & Leadership Engagement Act as a bridge between business stakeholders and engineering teams. Partner with leadership to prioritize AI investments and initiatives. Translate complex AI capabilities into business-focused insights and outcomes. Lead cross-functional teams and ensure delivery accountability. 5. AI Product Lifecycle Ownership Own full lifecycle: Ideation Architecture Deployment Monitoring Optimization Define and track KPIs and ROI for AI initiatives. Oversee vendor/tool selection (LLM providers, MLOps platforms, data tools). 6. Governance & Risk Management Ensure compliance with data privacy, security, and ethical AI standards. Identify risks and define mitigation strategies for enterprise AI adoption. Establish AI governance frameworks and best practices. Technical Skills Required Architecture & AI Systems LLMs, AI agents, RAG architectures, embedding pipelines Machine learning & deep learning systems Real-time + batch AI pipelines Cloud & Infrastructure Azure (preferred), AWS, or GCP Kubernetes, Docker Distributed systems & microservices design Data & Engineering Data pipelines (Spark, Kafka, Databricks, ADF) Strong SQL + Python MLOps MLflow / Azure ML / Vertex AI Model lifecycle management Experience Required 12 15+ years in AI, data, or engineering roles Experience in strategy consulting / enterprise transformation Proven experience delivering production AI systems at scale Exposure to CPG / manufacturing / supply chain domains (highly preferred) Success Metrics Business impact (ROI, cost savings, efficiency gains) Successful production deployment of AI solutions Adoption across business functions Stakeholder satisfaction and strategic alignment