AI Platform Developer

3 days ago

bareilly, India beBeePlatform Full time

As a Senior AI Platform DevOps Engineer, you will be responsible for developing and operating a highly available and scalable AI platform using technologies such as Qdrant, Anyscale, and Ray.Key Responsibilities:Platform Architecture: Designing and building a modular AI platformCore Infrastructure Development: Building essential APIs and infrastructure to power conversational applications and AI agentsLLM Operational Solutions: Implementing workflows for Large Language Models, including inference pipelines, fine-tuning, caching, and evaluationDeployment & Performance Optimization: Deploying AI services on cloud platforms with Kubernetes, Lambda, and ECS, ensuring scalability and resilience while optimizing vector databases and model runtimesCollaboration, Governance, & Mentorship: Partnering with engineering teams and research teams to deliver production-grade services for AI/RAG pipelines, establishing governance/security standards, and mentoring junior engineers in AI infrastructure best practicesRequirements:8+ years of experience as a Platform Engineer or Site Reliability/DevOps Engineer, with at least 3+ years in AI/ML platform development (MLOps)Deep expertise in Python, with strong design and debugging skillsAbility to work independently and lead complex projects with excellent problem-solving, analytical, and communication skillsProficiency working with cloud platforms such as AWS, GCP, or Azure and familiarity with MLOps/AI DevOps tools like MLflow or Kubeflow, proficient in CI/CD and infrastructure as code (Terraform/CloudFormation)Hands-on expertise with CI/CD pipelines, model observability, and incident response for AI/ML servicesPreferred Qualifications:Experience implementing and optimizing Platforms supporting large language model (LLM) pipelines with frameworks such as LangChain, LlamaIndex, Hugging Face Transformers, or similarHands-on knowledge of scaling and setting up Vector DB platforms such as Qdrant (or other vector DBs like Pinecone, Weaviate) for semantic search and embeddings managementExposure to MLOps tools, Ray.io, Anyscale, or other distributed orchestration & inference frameworksExperience with developing and deploying containerized applications using Docker and Kubernetes, including Helm charts and automated scalingUnderstanding of LLMOps patterns – model registry, prompt versioning, and feedback loops,

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