Senior AI platform Engineer

Job Summary (Senior AI Platform Engineer):

- Design, develop, and maintain production-grade AI systems, including LLM integrations, RAG pipelines, AI agent workflows, and supporting infrastructure.
- Build and optimize AI agent workflows and orchestration patterns (e.g., LangGraph).
- Develop and maintain RAG (Retrieval-Augmented Generation) systems , focusing on chunking, retrieval pipelines, embeddings, and response quality.
- Implement and manage LLM API integrations with robust retry logic, fallbacks, rate limiting, and cost optimization.
- Build advanced prompt engineering solutions (system prompts, few-shot learning, structured outputs, versioning).
- Develop input validation, output filtering, and AI safety layers to ensure secure and reliable AI operations.
- Implement AI observability and evaluation frameworks (tracing, regression testing, quality checks).

- Build data ingestion pipelines for AI systems, including document processing, embeddings, and vector storage.
- Collaborate with product and architecture teams to deliver scalable and reliable AI solutions.
- Write clean, testable, and production-ready code , including unit, integration, and AI-specific tests.
- Contribute to technical documentation, design documentation, and operational runbooks.

Must-Have Skills & Experience:

- Hands-on experience building AI/ML or LLM-powered production systems.
- Strong understanding of LLM fundamentals (tokens, embeddings, context windows, temperature, similarity search).
- Experience with RAG systems (indexing, chunking strategies, retrieval methods).
- Expertise in prompt engineering (few-shot, structured outputs, iterative improvements).
- Experience with AI orchestration frameworks (LangGraph, LangChain, or similar).
- Hands-on experience with vector databases (pgvector, Pinecone, Qdrant, Weaviate, etc.).
- Strong proficiency in Python.
- Experience working with LLM APIs (OpenAI, Claude, or similar).
- Understanding of AI safety concepts (prompt injection, jailbreaking, mitigation strategies).
- Experience with cloud platforms (AWS preferred) and containerization (Docker, Kubernetes).
- Strong analytical and problem-solving skills.

Good-to-Have Skills:

- Experience with AI observability tools (LangSmith, LangFuse).
- Familiarity with Model Context Protocol (MCP) or similar integration patterns.
- Understanding of fine-tuning vs. prompt-based approaches.
- Programming experience in Java.
- Experience with event-driven systems (Kafka, RabbitMQ).
- Telecom domain knowledge (billing, CRM, lifecycle management).
- Experience building customer-facing AI products.
- Backend development experience (Go, Node.js, Java microservices). Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.