Forward Deployment Engineer
Role details
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Tech stack
Job description
The Expert Senior Manager, Forward Deployed AI Engineer will architect, build, and scale next-generation generative AI systems and agentic solutions for Bain's clients. As a leader in the practice, you will sit at the intersection of advanced engineering, applied AI research, product strategy, and responsible AI governance. You will own the full lifecycle-from research and experimentation to production deployment and ongoing optimization-and guide teams across engineering, product, data science, ethics, and client stakeholders.
WHAT YOU'LL DO
- Design, build, and deploy end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.
- Architect multi-component pipelines, including:
- Retrieval-Augmented Generation (RAG)
- Fine-tuning and parameter-efficient tuning
- Embedding generation and optimization
- Hybrid retrieval strategies (vector, graph, keyword)
- Integrate reasoning, tool use, function calling, and orchestration across complex workflows
- Engineer advanced agentic systems, ensuring clear separation of concerns, robust memory architecture, and scalable tool ecosystems
- Lead everything from early-stage research, model experimentation, and evaluation design to production system deployment
- Oversee API development, microservices, CI/CD pipelines, observability, and cloud-native deployment
- Design for interoperability using emerging standards such as Model Context Protocol (MCP)
- Build scalable GenAIOps processes for automated testing, regression evaluation, latency monitoring, and continual improvement
- Balance performance, safety, responsible AI principles, and cost across system design:
- Implement guardrails, fallbacks, red-teaming strategies, and human-in-the-loop (HITL) workflows
- Partner with global ethics teams to ensure alignment with Bain's Responsible AI standards
- Build automated evaluation suites integrating user signals, continual learning cycles, and ongoing model updates
- Design and implement evaluation frameworks covering:
- Hallucination rate and factual consistency
- Relevance and precision/recall
- Latency, throughput, and system-level performance
- Cost tracking and efficiency
- Partner closely with product, engineering, data science, ethics, and infrastructure teams to build robust, compliant AI systems
- Originate, scope, and sell AI engagements from shaping proposals, building client relationships, to driving commercial growth for the practice
- Act as a thought partner to executives and clients on AI strategy, architecture decisions, emerging capabilities, and implementation roadmaps
- Mentor and upskill technical teams on best practices including RAG, agents, prompt engineering, and AI safety
Requirements
Do you have experience in System design?, Do you have a Master's degree?, * 8-12+ years in software engineering, ML engineering, or applied AI roles with significant hands-on building responsibilities
- German language proficiency at C1 level or higher
- Demonstrated experience leading complex, multi-stack generative AI programs from conception through production
- Strong executive communication skills with the ability to translate highly technical concepts to business stakeholders
- Track record of leading engineering teams, mentoring technical talent, and collaborating with diverse cross-functional groups
- Advanced prompt engineering, context engineering, and conversation design
- Strong expertise in evaluation design, experimentation frameworks, and data labeling strategies for LLM apps
- Designing for interoperability using emerging standards such as Model Context Protocol (MCP)
- Experience with common tools such as Claude Code, Codex, Cursor
- Deep experience with:
- Advanced RAG architectures (vector, hybrid, graph-based retrieval)
- Agentic architectures (multi-agent systems, tool selection, routing, memory, planning, reflection)
- ReAct, RLAIF, and other HITL + feedback loops.
- AI-Specific Tools & Frameworks - Orchestration frameworks, Vector and graph databases and Model + API ecosystems
- Tools such as Claude Code, Codex, Cursor
- Strong background in system design, architecture, and production-grade deployment
- Deep familiarity with cost optimization and computational tradeoffs for LLM workloads
- Comfort operating in high-ambiguity environments with collaborative cross-functional teams
- Clear ability to lead, mentor, and inspire technical teams
- Experience in client-facing consulting or enterprise transformation environments is a strong plus