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Role Overview:
We are looking for an accomplished Lead AI Engineer to design, build and productionize next-generation AI solutions across enterprise use cases. This is a highly hands-on role at the intersection of Generative AI, Agentic AI, software engineering and AI architecture.
The ideal candidate brings strong engineering foundations and has successfully taken GenAI solutions beyond experimentation and PoCs into scalable, secure and production-grade enterprise systems.
What You'll Do
- Architect, develop and productionize enterprise-scale Generative AI and AI/ML solutions.
- Design sophisticated LLM-powered applications using RAG, AI agents, tool/function calling, orchestration and multi-agent workflows.
- Build GenAI solutions leveraging frameworks such as LangGraph, LangChain, Semantic Kernel or equivalent.
- Design scalable AI services, backend applications and APIs using Python and modern engineering frameworks.
- Build solutions leveraging LLMs, embeddings, vector databases, knowledge graphs and semantic/knowledge layers.
- Integrate AI capabilities with Snowflake, enterprise data platforms, business applications, APIs and cloud ecosystems.
- Establish robust AI engineering practices covering LLM evaluation, observability, guardrails, security, latency, reliability and cost optimization.
- Design end-to-end AI/ML pipelines spanning experimentation, deployment, monitoring and continuous improvement.
- Evaluate emerging LLMs, agentic frameworks and AI technologies and translate them into practical enterprise solutions.
- Drive technical design and architecture decisions for AI platforms and products.
- Collaborate with product managers, data scientists, architects and business stakeholders to translate complex requirements into scalable AI solutions.
- Provide technical leadership, mentor engineers and establish strong engineering practices across the team.
What We're Looking For
- 10+ years of overall experience in software engineering, AI/ML, data or related technology roles, with significant recent experience building AI/GenAI solutions.
- Strong hands-on expertise in Python with excellent software engineering and system-design fundamentals.
- Proven experience designing, building and deploying LLM/Generative AI applications in production, rather than only PoCs or experimentation.
- Deep hands-on understanding of RAG, prompt engineering, embeddings, vector search, LLM orchestration and Agentic AI.
- Experience building AI agents, tool/function-calling workflows and multi-agent systems.
- Hands-on experience with one or more LLM ecosystems such as OpenAI, Azure OpenAI, Anthropic Claude or Google Gemini.
- Strong understanding of REST APIs, microservices, distributed systems, databases and enterprise application architecture.
- Experience with Snowflake and modern enterprise data ecosystems.
- Strong experience with at least one major cloud platform: AWS, Azure or GCP.
- Experience implementing LLM evaluation, monitoring, observability, security, responsible AI and guardrails.
- Ability to independently lead the technical design and implementation of sophisticated AI solutions.
- Strong problem-solving, stakeholder communication and technical leadership capabilities.
Good to Have
- Experience with knowledge graphs, ontologies, semantic layers or GraphRAG.
- Hands-on exposure to MLOps/LLMOps, model serving, CI/CD and AI observability platforms.
- Experience with vector databases such as Pinecone, Weaviate, Milvus, pgvector or Redis.
- Experience with fine-tuning, model adaptation and open-source LLMs.
- Experience with AI security, governance, privacy and enterprise AI architecture.
- Hands-on experience with frameworks/tools such as PyTorch, Hugging Face, LangGraph, LangChain or Semantic Kernel.
- Understanding of data engineering, distributed data processing, streaming architectures and modern data platforms.
- Experience with Snowflake Cortex, Cortex Search, Cortex Agents or related Snowflake AI capabilities.
- Prior experience mentoring senior engineers or technically leading engineering teams.
Education
- Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, Data Science or a related quantitative/technical discipline.
- Strong academic background, preferably from a premier engineering/technology institution with a demonstrated record of academic and technical excellence.
What Will Make You Stand Out
We are particularly interested in engineers who can demonstrate that they have personally architected and built production GenAI systems, rather than simply experimented with LLMs.
Strong candidates will be able to discuss real-world experience around:
- Production RAG and Agentic AI architectures
- Building scalable Python/API-based AI services
- LLM evaluation and observability
- Enterprise data and application integration
- Security and guardrails
- Latency and inference-cost optimization
- Taking an AI solution from prototype → production → scale
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
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