AI Solution Engineer

Role & responsibilities

Must Have:

• 2-5 years of professional experience in AI, Machine Learning, Data Science, and/or Automation roles.
• Hands-on experience with developing and deploying AI solutions, with expert understanding of Generative AI concepts (e.g., LLMs, RAG architecture) and/or Agentic AI principles.
• Proficiency in Python programming and experience with AI/ML libraries (e.g., LangChain, LlamaIndex, Hugging Face).
• Familiarity with cloud-based AI/ML services (Azure OpenAI, GCP Vertex AI, or AWS Bedrock) and basic understanding of deployment processes.
• Basic understanding of data pipelines and MLOps concepts.
• Experience with prompt engineering and optimization techniques for LLMs.
• Knowledge of vector databases (e.g., Pinecone, Weaviate, ChromaDB) for semantic search and retrieval.
• Understanding of AI agent architectures including planning, reasoning, and tool-use capabilities.
• Strong problem-solving skills and a keen interest in learning new technologies.
• Good communication and teamwork skills with ability to translate technical concepts to non-technical stakeholders.

Good to Have:

• Experience with specific Generative AI frameworks or libraries (LangGraph, AutoGen, CrewAI, Semantic Kernel).
• Exposure to building or working with multi-agent systems and agent orchestration patterns.
• Familiarity with document understanding techniques, such as data extraction, OCR, or intelligent document processing (IDP).
• Experience with fine-tuning or adapting foundation models for domain-specific tasks.
• Knowledge of evaluation frameworks and metrics for LLM applications (e.g., RAGAS, human-in-the-loop evaluation).
• Understanding of enterprise AI governance, responsible AI principles, and security best practices.
• Experience implementing memory systems and context management for conversational AI.
• Familiarity with streaming responses and real-time AI applications.
• Experience using modern AI development tools (e.g., GitHub Copilot, Cursor) to enhance productivity.
• Knowledge of function calling, tool integration, and API orchestration in agentic systems.
• Experience with A/B testing and continuous improvement of AI systems.
• Relevant coursework or certifications in AI/ML (e.g., Azure AI Engineer, AWS ML Specialty, Google Cloud ML Engineer).
• Exposure to accounting, finance, or ERP systems for domain-specific AI applications.