Title: Principal AI Engineer
Location: US(NYC)- 3(WFO)
About the Role
8–14 years of software engineering experience, with strong hands-on large-scale Python
Working depth in at least one systems or backend language Go, Rust, Java, or C/C++ and the judgment to know when to reach for it
Strong data structures and algorithms.
Strong understanding of APIs, microservices, and system design
Hands-on experience building and operating data pipelines and production-grade distributed systems.
Agentic AI and LLMs
2+ years of hands-on LLM engineering, with at least couple agentic system you designed and took to production
Production experience with agent frameworks LangGraph, Google ADK, CrewAI, Claude Agent SDK, or equivalent and the fluency to move between them as the ecosystem evolves
Experience building MCP (Model Context Protocol) servers and tool-calling interfaces
RAG from first principles: chunking strategy, embeddings, vector and hybrid retrieval, reranking, and response validation
Strong experience with vector databases (Milvus, Pinecone, Weaviate, FAISS, etc. or cloud equivalents)
Design of guardrails and reliability patterns validators, policy checks, self-correction loops, deterministic fallbacks, circuit breakers, and rollback paths
Optimization
Deep familiarity with token optimization and context-window management context shaping, pruning, and compaction
Latency and cost optimization through caching, model routing, batching, streaming, and parallel tool calls
Performance testing and tuning systems against defined SLOs
Evaluation
Experience building evaluation frameworks for LLM systems offline eval sets, continuous online evaluation, and regression detection
Instrumentation and traceability suitable for regulated enterprise environments using tools like LangSmith, Langfuse, etc.
Cloud
Hands-on AWS: containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift) and orchestration (Step Functions); Azure or GCP equivalents also valued
Familiarity with CI/CD pipelines and DevOps practices
Infrastructure as code with Terraform or CloudFormation, and mature CI/CD practice
Working traits
Strong analytical problem-solving with a bias to ownership and urgency
Clear cross-team communication, working directly with client stakeholders to translate business problems into technical roadmaps
Able to work productively in ambiguity from system-level documentation and ramp quickly in unfamiliar codebases
Good to Have
Experience with managed AI platforms Amazon Bedrock, Vertex AI, Azure AI paired with fluency in the underlying fundamentals
Roles & Responsibilities
Design and build agentic systems: Lead the architecture and implementation of tool-calling agents that combine retrieval, structured reasoning, and secure action execution with least-privilege access.
Productionize LLM applications: Build retrieval pipelines, prompt synthesis, response validation, and self-correction loops, backed by rigorous evaluation.
Own the full stack: Deliver the data pipelines, backend services, distributed compute, and orchestration layer that agentic systems depend on not only the model invocation.
Engineer for reliability and governance: Build validator models, adversarial test suites, and policy checks; enforce deterministic fallbacks and rollback strategies; instrument continuous evaluation.
Optimize for cost and latency: Drive measurable improvements in token efficiency, response time, and unit economics against defined SLOs.
Codebase ownership: Build, maintain, and review high-quality Python and SQL, with an emphasis on reusable components, scalability, and performance.
Cloud integration: Deploy AI applications on AWS, Azure, or GCP with optimized resource usage and robust CI/CD.
Cross-functional collaboration: Partner with product owners, data scientists, and business SMEs to define requirements and deliver impactful AI products.
Mentoring and technical leadership: Set engineering standards and share knowledge across the team, raising the bar on AI and software engineering practice.
