Principal AI Engineer

Employment Type: Full Time (Overlapping EST)

Experience Level: Staff/Principal (8–14 years)

• 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.

• 2+ years of hands‑on LLM engineering, with at least couple agentic system you designed and took to production
• Production experience with agent frameworks — Lang Graph, 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

• 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

• 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 Lang Smith, Langfuse, etc.

• 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 Dev Ops practices
• Infrastructure as code with Terraform or Cloud Formation, and mature CI/CD practice

• 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

• 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.