AI/ML Engineer
BackOps AI6 days ago
Responsibilities
- Architect and build AI/ML infrastructure supporting model serving, inference pipelines, model routing, orchestration, experimentation, evaluation, deployment, versioning, and lifecycle management.
- Optimize AI execution for latency, throughput, scalability, inference cost, and production reliability.
- Develop systems for model evaluation, benchmarking, comparative testing, context engineering, retrieval, fine-tuning, adaptation, and continuous improvement.
- Build production reliability capabilities including observability, failure detection, recovery mechanisms, performance monitoring, and operational controls.
- Establish metrics and feedback loops for model quality, inference performance, cost, reliability, regression detection, resource utilization, and deployment efficiency.
- Partner with product and engineering teams to connect technical improvements with accurate decisions, reliable execution, and autonomous resolution outcomes.
Requirements
- 6+ years of relevant software engineering experience with meaningful ownership of complex technical systems at the Senior or Staff level.
- Experience building, deploying, or optimizing production AI/ML infrastructure, model-serving systems, inference pipelines, or similarly sophisticated AI systems.
- Strong proficiency in Python and experience designing and operating reliable distributed systems.
- Understanding of foundation-model tradeoffs involving model selection, inference architecture, quality, latency, throughput, scalability, and cost.
- Hands-on experience with cloud infrastructure, containerization, orchestration, and production AI workload operations.
- Understanding of model evaluation and experimentation with the ability to connect technical changes to measurable improvements.
- Ability to work across architecture, implementation, performance debugging, and production operations in rapidly changing technical environments.
- Preferred experience with PyTorch, Hugging Face, vLLM, Triton, Ray, Kubernetes, or comparable model-serving and distributed-computing frameworks.
- Preferred experience designing model optimization, fine-tuning, post-training, evaluation, continuous-improvement, agentic AI, or multi-step autonomous-workflow infrastructure.
- Supply chain or logistics experience is useful but not required.
Tech Stack
Categories
About BackOps AI
BackOps AI builds an agentic AI platform that automates supply‑chain workflows and decision-making for logistics and operations teams. The company sells enterprise software and integrations that take actions within live operations to improve efficiency and reliability. Founded in 2024 and headquartered in San Francisco, it is privately held and focuses on supply chain, business process automation, and AI-driven operations.