Backend AI Engineer

7+ years of software engineering experience with strong backend ownership. • Hands-on AI/ML engineering experience with LLM systems in production. • Deep practical knowledge of Agentic AI frameworks and multi-step workflow engines. • Strong experience with RAG architecture and vector search technologies (FAISS, Pinecone, Weaviate, pgvector, Milvus). • Hands-on experience in LLM training and fine-tuning workflows (instruction tuning, domain adaptation, PEFT/LoRA/QLoRA, and parameter-efficient methods). • Python (primary) with API frameworks such as FastAPI/Flask and asynchronous programming patterns. • Containerization and orchestration with Docker and Kubernetes (Helm, ingress, secrets, HPA, resource quotas, monitoring). • CI/CD ownership using GitHub Actions/GitLab CI/Jenkins and infrastructure-as-code patterns (Terraform/Ansible/Helm). • Datastores and caching: PostgreSQL, Redis, and object storage, with exposure to NoSQL where needed. • Cloud fundamentals on AWS/GCP/Azure (compute, container registries, IAM, networking, managed databases). • Experience with observability stack (Prometheus, Grafana, OpenTelemetry, ELK/Opensearch, alerting). Preferred / Plus • Experience with MLflow, DVC, Weights & Biases, or equivalent experiment and dataset lifecycle tooling. • Experience with serving stacks: Triton, vLLM, TorchServe, Text Generation Inference, BentoML, or equivalent. • Experience with agent memory stores, retrieval quality benchmarking, and policy/safety layers for autonomous agents. • Familiarity with Terraform, ArgoCD, or GitOps workflows for AI platform delivery. • Security/compliance and governance awareness for AI systems (data privacy, prompt-injection controls, and auditability). What We Expect • Own complex AI backend features from design to production with measurable impact. • Design systems for reliability, observability, and cost-aware scaling. • Drive trade-o