Sr. AI Platform Engineer

Location

501, Fifth Avenue, Suite 805 New York, NY 10017

• 8+ years of experience as a Platform Engineer (Site Reliability / Dev Ops), with at least 3+ years in AI/ML platform development (MLOps).
• Deep expertise in Python, with strong design and debugging skills.
• Ability to work independently and lead complex projects with excellent problem‑solving, analytical, and communication skills.
• Proficiency with cloud platforms such as AWS, GCP, or Azure and familiarity with MLOps/AI Dev Ops tools like MLflow or Kubeflow, proficient in CI/CD, infrastructure as code (Terraform / Cloud Formation).
• Hands‑on expertise with CI/CD pipelines, model observability, and incident response for AI/ML services.

• Experience implementing and optimizing platforms supporting large language model (LLM) pipelines with frameworks such as Lang Chain, Llama Index, Hugging Face Transformers, or similar.
• Hands‑on knowledge of scaling & setting up vector database platforms such as Qdrant (or other vector DBs like Pinecone, Weaviate) for semantic search and embeddings management.
• Exposure to MLOps tools, Ray.io, Anyscale, or other distributed orchestration & inference frameworks.
• Experience with developing and deploying containerized applications using Docker and Kubernetes, including Helm charts and automated scaling.
• Understanding of LLMOps patterns — model registry, prompt versioning, and feedback loops.

• Platform Design and Architecture: build and operate a highly available, scalable, modular AI platform using technologies such as Qdrant, Anyscale, and Ray to support LLM orchestration, vector search, and multi‑agent frameworks.
• Core Infrastructure Development: build essential APIs and infrastructure to power conversational applications, AI agents, and analytics tools.
• LLM Operational Solutions: implement workflows for large language models, including inference pipelines, fine‑tuning, caching, and evaluation for open‑weight and hosted models.
• Deployment & Performance Optimization: deploy AI services on AWS with Kubernetes (EKS), Lambda, and ECS, ensuring scalability and resilience while optimizing vector databases and model runtimes for cost and performance.
• Collaboration, Governance, & Mentorship: partner with engineering teams and research teams to deliver production‑grade, self‑healing, and performance‑optimized services for AI/RAG pipelines, establish governance/security standards, and mentor junior engineers in AI infrastructure best practices & reviews.

Sr. AI Platform Engineer