Forward Deployed AI Engineer
- 8+ years of professional software development experience, with a deep understanding of full-stack architectures.
- Hands-on experience delivering production AI/ML systems, including generative AI or agentic AI applications.
- Strong proficiency with AI coding tools such as Claude Code, Cursor, Codex, or GitHub Copilot.
- Deep understanding of agentic architecture patterns, including single- and multi-agent workflows, supervisor/worker patterns, state and memory management, orchestration, and safe action execution.
- Strong knowledge of modern GenAI patterns, including advanced RAG, embeddings, vector and hybrid search, reranking, tool/function calling, structured outputs, and context engineering.
- Proficiency in Python and AI engineering frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent.
- Working knowledge of cloud AI/ML platforms such as Amazon Bedrock, Azure AI Foundry, and Vertex AI.
- Familiarity with enterprise data platforms such as Snowflake, Databricks, and vector databases.
- Experience establishing LLMOps/MLOps practices, including versioning, CI/CD, containerization, and cost and performance optimization.
- Ability to structure codebases and specifications to produce reliable, context-efficient AI outputs.
- Strong judgment in architecture trade-offs, model selection, retrieval strategies, latency, cost, security, and vendor lock-in.
- Ability to explain technical decisions clearly to technical and non-technical stakeholders.
- Comfortable engaging with executives and frontline users.
- Strong attention to detail, ownership, problem-solving, and a proactive mindset.
- Experience working with Agile/Scrum teams and large, complex systems.
- Assess AI feasibility and define technical direction for AI initiatives, including data products, retrieval pipelines, model orchestration, agentic workflows, evaluation, deployment, and monitoring.
- Design and build LLM workflows, retrieval systems, and agentic applications using enterprise data and infrastructure.
- Define secure, scalable architectures across cloud and AI platforms.
- Establish evaluation and observability standards, including automated evaluations, tracing, quality scoring, latency and cost monitoring, and regression testing.
- Implement human-in-the-loop controls and sound data security practices.
- Develop prototypes that can evolve into production systems and transfer technical knowledge to teams responsible for ongoing maintenance and scaling.
- Evaluate emerging models, frameworks, and tools, recommending practical solutions based on business needs.
- Optimize AI systems for reliability, security, performance, and cost efficiency.
- Collaborate with technical teams, executives, and end users to align AI solutions with business objectives.
- Support knowledge transfer and help teams develop the capabilities needed to maintain and scale AI solutions.
- Experience implementing AI solutions across different enterprise environments and technology stacks.
- Experience transferring AI solutions to internal teams and supporting their adoption and scaling.
- Familiarity with advanced AI evaluation frameworks, observability platforms, and automated quality assessment tools.
- Experience establishing AI engineering practices in environments with limited existing infrastructure or governance.
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