AI Engineer

Required Skills and Experience

•             Engineering Foundation: 5+ years of software engineering experience with a deep understanding of design patterns, clean architecture, and distributed systems.

•             AI/ML Expertise:

o            Hands-on experience integrating LLMs into production applications.

o            Proficiency in orchestration frameworks (LangChain, LangGraph, PydanticAI, etc.).

o            Deep understanding of RAG architectures, vector databases (e.g., Pinecone, Milvus), and prompt engineering.

•             Backend Mastery: Expert-level proficiency in Python (FastAPI/Flask). Strong experience building RESTful and async, event-driven architectures (message queues/event buses).

•             DevOps & Infrastructure:

o            IaC: Proven experience with Terraform, AWS CloudFormation, or Pulumi.

o            CI/CD: Experience building production-grade deployment pipelines.

o            Cloud: Hands-on experience with major cloud platforms (AWS, Azure, or Google Cloud Platform).

•             Observability: Practical experience configuring and maintaining monitoring stacks (Datadog, Grafana, Prometheus) to track application performance, errors, and LLM-specific telemetry (cost/latency).

•             Security: Strong background in secure API development, including implementing and managing OAuth 2.0 and mTLS.

•             Leadership: Demonstrated ability to lead technical initiatives, mentor engineers, and communicate complex trade-offs to non-technical stakeholders.