AI Engineering – Senior/Lead Engineer

Job Description

REQUIREMENTS

• Software engineering experience, with a demonstrated track record of shipping complex, production-grade systems and building applications powered by LLMs or other foundation models.
• Hands-on experience designing and deploying LLM-based features in production including prompt and context engineering, tool/function calling, agentic workflows, and evaluation-driven iteration.
• Strong coding skills in Python and/or TypeScript, with experience using modern AI SDKs and frameworks (e.g., the Anthropic, OpenAI, or Google SDKs; LangChain, LlamaIndex, LangGraph; agent frameworks; MCP).
• Solid working knowledge of how foundation models behave in practice — including their capabilities and failure modes and experience with fine-tuning, distillation, or model adaptation when product needs warrant it.
• Familiarity with the architectural patterns of production LLM systems, including orchestration, tool use, memory, guardrails, observability, caching, and cost/latency optimization.
• Experience with embeddings and information retrieval; hands-on experience with Retrieval-Augmented Generation (RAG) architectures and vector stores (e.g., Pinecone, Weaviate, pgvector) is strongly preferred.
• Experience building offline and online evaluation pipelines for AI systems defining metrics, building eval sets, running A/B tests, and using signals to drive iterative improvement.
• Strong problem-solving and communication skills, with the ability to mentor peers, influence technical direction, and collaborate effectively across engineering, product, and data science teams.
• Bachelor’s/Mastersdegree in Computer Science, a related technical field, or equivalent practical experience. Advanced degrees and open-source contributions are a plus.

RESPONSIBILTIES

• Design and build production AI systems — including agents, RAG pipelines, and LLM-powered workflows — that integrate seamlessly into our client’s analytics and decision management platform.
• Translate product requirements into technical designs, balancing model capabilities, latency, cost, and reliability against real-world business constraints.
• Develop robust evaluation frameworks and benchmarks to measure quality, safety, and regression across LLM-based features, and use those signals to drive iterative improvement.
• Drive end-to-end delivery of AI features, including prompt and context engineering, tool/function design, writing reusable and well-tested code, running offline and online evaluations, and communicating results to stakeholders.
• Build and operate the application layer around foundation models: orchestration, tool use, memory, retrieval, guardrails, observability, and human-in-the-loop workflows.
• Fine-tune, distill, and adapt open and closed foundation models when warranted, and align technical choices with our client’s product strategy and roadmap.
• Optimize inference performance, throughput, and cost across the serving stack — including caching, batching, routing, and model selection strategies.
• Apply modern AI engineering practices across heterogeneous infrastructure, from CPU-bound orchestration services to GPU-accelerated inference and training workloads.

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