AI Engineer - 504

Must Haves • 5+ years of software development in one or more languages (Python, C/C++, Go, Java); strong hands-on experience building and maintaining large-scale Python applications preferred. • 3+ years designing, architecting, testing, and launching production ML systems, including model deployment/serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows. • Practical experience with Large Language Models (LLMs): API integration, prompt engineering, fine-tuning/adaptation, and building applications using RAG and tool-using agents (vector retrieval, function calling, secure tool execution). • Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude). • Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to deliver efficient and reliable solutions. • Strong analytical problem-solving, ownership, and urgency; ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact. Nice To Haves • Proficiency building and operating on cloud infrastructure (ideally AWS), including containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift), orchestration (Step Functions), model serving (SageMaker), and infra-as-code (Terraform/CloudFormation). Responsibilities • Build agentic AI systems: Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following MCP protocol. Engineer robust guardrails for safety, compliance, and least-privilege access. • Productionize LLMs: Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations. • Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability. • Collaborate directly with users: Partner with production engineers, and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability, risk reduction, and cost; and deliver auditable, business-aligned outcomes. • Safety, reliability, and governance: Build validator models, adversarial prompts, and policy checks into the stack; enforce deterministic fallbacks, circuit breakers, and rollback strategies; instrument continuous evaluations for usefulness, correctness, and risk. • Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool-calls to meet stringent SLOs under real-world load. • Build a RAG pipeline: Curate domain-knowledge; build data-quality validation framework; establish feedback loops and milestone framework maintain knowledge freshness. • Raise the bar: Drive design reviews, experiment rigor, and high-quality engineering practices; mentor peers on agent architectures, evaluation methodologies, and safe deployment patterns