AI Solution Expert
7 days ago
Position : AI Expert
Experience Level : 10+ Years
Location : Remote
About The Role
We are seeking an experienced AI Expert with strong expertise in Retrieval-Augmented Generation (RAG), retrieval efficiency optimization, and the design & implementation of agentic AI platforms. The ideal candidate will have hands-on experience in building production-grade AI systems that combine large language models (LLMs) with enterprise data, tools, and workflows.
This role involves working closely with product, engineering, and data teams to design, implement, and optimize AI-driven applications that are scalable, accurate, and business-ready.
Key Responsibilities
- Design and implement RAG pipelines using state-of-the-art LLMs and vector databases.
- Optimize retrieval efficiency through hybrid search, re-ranking, embeddings fine-tuning, and metadata enrichment.
- Architect and deploy agentic AI platforms, enabling autonomous agents to interact with tools, APIs, and enterprise data.
- Collaborate with engineering teams to integrate AI workflows into production applications.
- Define and track evaluation metrics (accuracy, groundedness, helpfulness, relevance, etc.) for AI responses.
- Ensure scalability, robustness, and compliance of AI systems in real-world deployments.
- Stay up to date with advancements in LLMs, retrieval frameworks, and orchestration tools (LangChain, LlamaIndex, DSPy, etc.).
- Provide technical leadership and mentor team members in AI/ML best practices.
Required Skills & Qualifications
- Bachelors/Masters in Computer Science, AI/ML, Data Science, or related field.
- Proven experience with LLMs and retrieval-augmented systems.
- Hands-on expertise with vector databases (Pinecone, Weaviate, Milvus, FAISS, etc.) and search optimization techniques.
- Strong proficiency in Python and frameworks like LangChain, LlamaIndex, DSPy (or equivalent).
- Deep understanding of agentic workflows, tool orchestration, and AI-driven automation.
- Experience in designing evaluation frameworks (e.g., clarity, coherence, fluency, groundedness, tool effectiveness).
- Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).
- Strong problem-solving skills and ability to translate research into practical business solutions.
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