Senior AI Engineer

We're hiring a Software Engineer to build the infrastructure that powers our AI agents and ML systems end-to-end — from fine-tuning foundation models to shipping production-grade agent harnesses. You'll work across the stack: building MLOps pipelines, customizing LLMs, and deploying scalable agent systems on Kubernetes. This role sits at the intersection of ML engineering, platform engineering, and applied AI.

• Design and build agent harnesses in Python — the runtime scaffolding that enables AI agents to perceive, reason, plan, and act reliably
• Develop and maintain a robust MLOps framework using Kubeflow and complementary tooling (MLflow, Argo, Airflow, or similar) to orchestrate training, evaluation, and deployment workflows
• Fine-tune foundation LLMs using techniques such as LoRA/QLoRA, SFT, and RLHF; manage datasets, training runs, and evaluation pipelines
• Deploy and operate services on Kubernetes, including model serving, autoscaling, and observability
• Build and integrate AI agents using modern agent frameworks (LangGraph, CrewAI, AutoGen, LlamaIndex, or similar)
• Apply software engineering rigor — SOLID principles, secure coding, static analysis, code reviews, and CI/CD — across all deliverables
• Collaborate with researchers, ML engineers, and product teams to take prototypes from notebook to production

• Strong Python engineering skills with a track record of building production systems
• Hands-on experience building agent harnesses or agentic systems using at least one framework (LangGraph, CrewAI, AutoGen, LangChain, LlamaIndex, etc.)
• Experience designing or contributing to MLOps pipelines, with working knowledge of Kubeflow
• Practical experience fine-tuning foundation LLMs (open-source models such as Llama, Mistral, Qwen, or similar)
• Proficiency deploying containerized workloads on Kubernetes (Helm, operators, networking, resource management)
• Working knowledge of at least one major cloud provider (AWS, GCP, or Azure) — compute, storage, IAM, and managed ML services
• Solid grasp of software engineering best practices: SOLID, design patterns, secure coding (OWASP), static analysis (e.g., mypy, ruff, Bandit, SonarQube), unit/integration testing

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience

   

Nice to Have

• Experience with distributed training (DeepSpeed, FSDP, Accelerate)
• Familiarity with vector databases, RAG architectures, and evaluation frameworks for LLMs
• Experience with model serving frameworks (vLLM, TGI, KServe, Triton)