Role overview
- Set the technical direction for AI Engineering across foundation model integration, fine-tuning pipelines, RAG systems, agentic workflows, and evaluation infrastructure.
- Own the most complex and ambiguous AI engineering problems in the company, from initial design through production deployment and ongoing optimization.
- Establish engineering standards for model development, prompt management, evaluation, deployment, and observability that the rest of the AI organization adopts.
- Lead architecture reviews and serve as the senior technical reviewer for high-stakes AI initiatives.
- Design and build production-grade Generative AI systems including retrieval-augmented generation, multi-agent orchestration, tool-using agents, and domain-adapted models.
- Develop fine-tuning, distillation, and post-training pipelines using techniques such as SFT, DPO, RLHF, and parameter-efficient methods (LoRA, QLoRA, adapters).
- Architect and implement vector retrieval systems, semantic search, and hybrid retrieval pipelines optimized for accuracy, latency, and cost.
- Build robust evaluation frameworks covering automated metrics, LLM-as-judge, human review, regression testing, and safety evaluations.
- Design and build the AI platform that powers internal teams, including model serving infrastructure, prompt and prompt-template management, experiment tracking, and feature stores.
- Optimize inference performance across latency, throughput, and cost, including quantization, batching, caching, speculative decoding, and intelligent routing across model providers.
- Establish LLMOps practices for continuous evaluation, drift detection, prompt versioning, rollback strategies, and incident response.
- Partner with platform and infrastructure teams to ensure AI workloads run reliably on GPU and accelerator hardware across cloud environments.
- Stay current with the rapidly evolving AI research landscape and identify which advances translate into production value for the business.
- Prototype emerging techniques (new model architectures, training methods, agent frameworks) and lead the path from experiment to production system.
- Contribute to internal technical strategy on build versus buy decisions for foundation models, vector databases, agent frameworks, and AI tooling.
- Partner with product, data science, research, and business stakeholders to scope AI initiatives and shape solutions that deliver measurable business impact.
- Mentor senior and staff engineers, raising the technical bar across the AI organization.
- Represent AI Engineering in executive forums, customer conversations, vendor evaluations, and industry engagements.
- Author technical documents, design docs, and (where appropriate) external publications that contribute to the broader AI community.
Basic qualifications
- 12+ years of software engineering experience, with 6+ years focused on machine learning or AI systems and 2+ years building production Generative AI applications.
- Demonstrated ownership of large-scale AI systems in production, including responsibility for latency, cost, accuracy, and reliability outcomes.
- Deep hands-on expertise in Python and modern ML frameworks (PyTorch, TensorFlow, JAX, Hugging Face Transformers).
- Strong command of LLM application development, including RAG architectures, prompt engineering, function calling, structured outputs, and agentic patterns.
- Experience with model fine-tuning, evaluation, and deployment lifecycles across at least one major cloud platform (GCP, Azure, or AWS).
- Proven ability to design distributed systems, including familiarity with vector databases, message queues, container orchestration, and observability stacks.
- Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or a related quantitative discipline. PhD welcomed but not required.
Preferred qualifications
- Experience training, fine-tuning, or post-training foundation models using techniques such as SFT, DPO, RLHF, RLAIF, or constitutional methods.
- Familiarity with agentic frameworks (LangChain, LangGraph, AutoGen, CrewAI, custom orchestration) and multi-agent system design patterns.
- Background in Voice AI, speech systems, multimodal models, or computer vision applied at production scale.
- Contributions to open source AI projects, peer-reviewed publications, or notable conference presentations.
- Experience in regulated or high-stakes domains (life sciences, healthcare, financial services) where accuracy, safety, and governance requirements are stringent.
- Familiarity with responsible AI practices including red-teaming, jailbreak resistance, content safety, bias evaluation, and AI governance frameworks.
- Typically requires a minimum of 15 years of related experience with a Bachelor’s degree; or 12 years and a Master’s degree; or a PhD with 8 years experience; or equivalent experience.
- Technical Depth: Expert-level mastery of AI engineering with the ability to operate from research papers down to production code.
- Systems Thinking: Comfort designing systems that span multiple services, data stores, model providers, and failure modes.
- Pragmatism: Strong instinct for when to build, when to buy, and when to wait, with a track record of avoiding over-engineering.
- Communication: Ability to explain complex AI concepts to executives, write design docs that drive decisions, and influence peers across disciplines.
- Builder's Mindset: Genuine enjoyment of writing code and solving hard technical problems, not just reviewing or directing others.
- Curiosity and Continuous Learning: Active engagement with the AI research landscape and a habit of trying new things.
Tags & focus areas
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