AI Infrastructure Leader

2 days ago

varanasi, India beBeeEngineer Full time

We're seeking a seasoned technologist to spearhead the development of our AI infrastructure. As a Senior AI Platform Engineer, you'll collaborate with research teams to deliver cutting-edge, production-grade services for AI pipelines.You'll design and build highly available, scalable, modular AI platforms using Qdrant, Anyscale, and Ray to support LLM orchestration, vector search, and multi-agent frameworks.Develop essential APIs and infrastructure to power conversational applications, AI agents, and analytics tools.Implement workflows for Large Language Models, including inference pipelines, fine-tuning, caching, and evaluation for open-weight and hosted models.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.Partner with engineering teams to establish governance/security standards and mentor junior engineers in AI infrastructure best practices & reviews.Requirements8+ years of experience as Platform Engineer (Site Reliability / DevOps Engineer) with at least 3+ years in AI/ML platform development (MLOps).Deep expertise in Python, strong design and debugging skills.Ability to work independently and lead complex projects with Excellent problem-solving, analytical, and communication skills.Proficiency working with cloud platforms like AWS, GCP, or Azure and familiarity with MLOps/AI DevOps tools like MLflow or Kubeflow.Hands-on expertise with CI/CD pipelines, model observability, and incident response for AI/ML services.Preferred QualificationsExperience implementing and optimizing Platforms supporting large language model (LLM) pipelines with frameworks such as LangChain, LlamaIndex, Hugging Face Transformers, or similar.Hands-on knowledge of Scaling & Setting up Vector DB 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.A Perfect FitThe ideal candidate will have a deep understanding of AI/ML platform development and be able to design, implement, and optimize cutting-edge systems. If you're passionate about leveraging technology to drive innovation, we encourage you to apply.

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