AI Engineer II
• Implement and continuously refine prompt engineering strategies, including prompt chaining, evaluation, and guardrails to improve accuracy and reliability
• Build and maintain RAG (Retrieval-Augmented Generation) pipelines, including Data ingestion and indexing, Embedding generation, Retrieval optimization & Response grounding and validation
• Development of scalable backend services using Python frameworks for data processing
• Design and manage embedding pipelines and vector search infrastructure using tools such as Pinecone, FAISS, Weaviate, Databricks or Snowflake
• Integrate LLM capabilities from platforms like OpenAI, Anthropic, and Microsoft into enterprise applications
• Build and maintain scalable APIs and microservices to expose AI capabilities across systems and applications
• Collaborate with data engineering and platform teams to ensure reliable data pipelines and access to high-quality data sources
• Deploy, monitor, and optimize AI solutions in cloud environments (Azure, AWS, GCP), ensuring performance, scalability, and cost efficiency
• Implement evaluation frameworks and monitoring for LLM outputs, including quality metrics, drift detection, and feedback loops
• Own the administration, provisioning, and ongoing optimization of the enterprise AI tool ecosystem, ensuring secure access controls and optimal functionality for development teams
• Troubleshoot and resolve issues in distributed AI systems, ensuring high availability and reliability
• Partner with business stakeholders to translate requirements into AI-driven solutions and automation opportunities
• Document architecture, design decisions, and best practices to support scalability and team adoption
- Bachelor’s degree in computer science, Statistics, Data Science, Information Systems or related field
- 5+ years of experience with AI technologies, particularly GenAI, LLM, Agentic AI, or MLOps, and delivering enterprise-scale, production-grade solutions
- Hands-on experience in building agentic AI systems, including multi-agent workflows, orchestration, and tool integration using frameworks such as AutoGen or CrewAI and failure handling
- Experience deploying and scaling AI solutions on platforms including Azure AI Foundry, Copilot Studio, or Google Vertex AI
- Hands-on experience in machine learning and deep learning frameworks such as TensorFlow, PyTorch, and scikit-learn.
- Expertise in Generative AI and LLM application development using OpenAI and Anthropic APIs, including:
- Advanced prompt engineering and optimization techniques
- Design and implementation of Retrieval-Augmented Generation (RAG) architectures
- Embeddings, semantic search, and contextual retrieval strategies
- Experience with vector databases such as Pinecone, FAISS, Weaviate or AI data cloud platform such as Databricks or Snowflake
- Advanced prompt engineering and optimization techniques
- Strong background in API development, microservices architecture, and distributed systems, enabling scalable AI solution deployment
- Experience with knowledge graphs, graph databases, or hybrid retrieval systems to enhance contextual reasoning and data relationships
- Proficiency in Python programming
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