01Overview
About the job
Required Skills and Technologies
• 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-off decisions across model quality, latency, and infrastructure costs.
• Communicate clearly with engineering, product, and leadership, and mentor other team members.
