AI Engineer
bmc
Job Description
- Design, build, and evolve agentic AI systems that reason, plan, execute, and adapt in production environments.
- Take AI-driven features from concept to production in a true 0–1 product environment.
- Write and review high-quality production code (Python-first) across AI pipelines, inference services, orchestration layers, and supporting systems.
- Implement prompt engineering, tool use, memory, evaluation, and guardrails as first-class engineering concerns, not experiments.
- Design agent frameworks that balance autonomy with determinism, observability, and safety.
- Make pragmatic architectural trade-offs across latency, cost, accuracy, scalability, and maintainability.
- Integrate and operate LLMs (commercial and/or open-source) including model selection, fine-tuning strategies, embeddings, retrieval (RAG), and inference optimization.
- Address real-world issues: hallucinations, drift, prompt regressions, failure modes, and customer trust.
- Deploy and operate AI services across cloud platforms (AWS, Azure, GCP), including secure enterprise integrations and customer-specific deployments.
- Design scalable inference and orchestration architectures using containers, APIs, and distributed systems.
- Ensure the platform is shippable, debuggable, and supportable — not fragile or research-grade.
- Act with founder-level ownership: identify gaps, propose solutions, and move forward without waiting for perfect requirements.