Principal Engineer, Artificial Intelligence

About the Role

Drive hands‑on delivery of AI and Generative AI solutions that streamline workflows and deliver measurable business value—measured by hours saved and the breadth of users served. You will architect, develop, and maintain production‑grade systems encompassing RAG pipelines, agentic tools, model routing, vector search, evaluation and guardrails, and observability—tightly integrated with internal platforms and enterprise datasets.

• Automate high‑impact workflows for internal stakeholders, prioritizing initiatives with the greatest time savings and broadest user reach.
• Deliver production‑ready copilots and customer‑facing applications for knowledge search, document summarization, intelligent recommendations, conversational analytics, and end‑to‑end workflow automation.
• Establish operational excellence through rigorous SLAs, latency and throughput optimization, robust safety and guardrail mechanisms, transparent evaluation frameworks, and cost‑efficient inference strategies.
• Architect and develop scalable, high‑performance data and AI systems that support GenAI use cases including RAG, agentic workflows, and model orchestration.
• Own the complete solution lifecycle: problem definition rapid prototyping rigorous evaluation production deployment ongoing monitoring.
• Implement guardrails (content policies, safety filters), prompt and version management, latency and throughput tuning, cost controls, load balancing, and fallback or model‑routing strategies.
• Design and implement RAG pipelines over heterogeneous and often messy datasets—including requirements documents, lessons learned, business rules, and unstructured content.
• Select appropriate embedding strategies, chunking approaches, vector search configurations, rerankers, and routing policies to maximize retrieval quality.
• Develop agentic workflows leveraging Lang Chain, Llama Index, MCP, and agent‑to‑agent (A2A) protocols; build tooling for agentic coding use cases.
• Translate subject‑matter‑expert knowledge into robust, maintainable prompts; evaluate trade‑offs between fine‑tuning and prompt engineering.
• Work hands‑on with large language models, vector databases (Pinecone, FAISS), and agent memory systems.
• Containerize applications with Docker, orchestrate with Kubernetes, and automate CI/CD pipelines; manage infrastructure as code (e.g. Terraform).
• Establish observability (Datadog, Grafana, Lang Fuse), evaluation frameworks, and model/data governance and access controls appropriate for internal enterprise environments.
• Bring experience building and maintaining data lakes and warehouses (Snowflake, Delta Lake, Big Query, MS Fabric).
• Build internal copilots and customer‑facing features using React, Node.js, and Python with REST or Graph
  
  QL backends.
• Collaborate closely with requirements, testing, validation, and platform teams; thrive in a fast‑paced environment with clear, proactive communication and rapid iteration.

• 8+ years of experience building production software, ideally including experience with ML systems, including hands‑on work with LLMs and Generative AI.
• Programming:
  Python (FastAPI, Num Py, Pandas, scikit‑learn, Pydantic, Jinja2) and Node.js; strong proficiency with APIs and distributed systems.
• LLMs & Frameworks:
  Hands‑on experience with at least one major deep learning or LLM stack (e.g., PyTorch/Transformers, Tensor Flow/Keras) and orchestration frameworks such as Lang Chain or Llama Index.
• Model Providers:
  Working familiarity with conncting to inference providers e.g. AWS Bedrock, along with OpenAI, Anthropic, Meta/Llama, and Mistral model ecosystems.
• Data & Storage: SQL and No
  
  SQL databases (Postgre
  
  SQL, Dynamo
  
  DB), Elasticsearch for search and analytics, and vector databases (Pinecone, Weaviate, FAISS, Milvus, pgvector).
• Cloud & Infrastructure: AWS (S3, EC2, Lambda, Cloud Watch, Fargate, EKS/ECS), Azure, GCP, Databricks, Docker, Kubernetes, Terraform, CI/CD, Airflow, and Kafka.
• Operational Excellence:
  Load balancing, monitoring and alerting (Datadog, Grafana, Lang Fuse), debugging production issues, and cost/performance optimization.
• Soft Skills:
  
  Strong communication abilities, product‑oriented…