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Job Description
Job Overview:
- In the role of AI ML Engineer, you will be integral in developing and implementing the AI features that drive our agentic AI platform.
- Your collaboration will be essential with AI ML architects, the founding team, data engineers, and platform engineers in creating agent workflows, retrieval pipelines, evaluation processes, and deployable AI components.
- This position demands a hands-on engineering approach, perfect for individuals adept at working with contemporary AI, ML, and LLM frameworks, as well as agentic AI systems, experimenting with models and prompts, and transforming design patterns into operational software.
- The opportunity is tailored for engineers eager to engage with practical enterprise AI systems instead of working solely on standalone demonstrations.
Main Duties:
- Construct and refine AI agent workflows utilizing frameworks like LangGraph, LangChain, AutoGen, CrewAI, or similar technologies.
- Develop AI agents for tasks such as data discovery, profiling, enrichment, extraction, classification, contextual reasoning, and enterprise knowledge discovery.
- Create retrieval-augmented generation pipelines that encompass document chunking, embedding generation, metadata tagging, vector indexing, retrieval tuning, and response generation.
- Engage with vector databases including Pinecone, Milvus, Qdrant, Weaviate, pgvector, Chroma, or comparable technologies.
- Design structured data agents capable of interfacing with databases, inspecting schemas, generating SQL queries, and facilitating semantic understanding of enterprise data.
- Execute document intelligence workflows for various formats including PDFs, Word documents, emails, transcripts, logs, and semi-structured enterprise content using tools like Azure Document Intelligence, AWS Textract, LlamaParse, or similar solutions.
- Assist in prompt engineering, testing, versioning, and the development of reusable prompt templates.
- Establish AI evaluation protocols that encompass answer quality, retrieval quality, hallucination checks, regression testing, robustness assessments, and response consistency analysis.
- Facilitate integration with LLM APIs and open-source models from platforms such as OpenAI, Anthropic, Azure OpenAI, Hugging Face, Llama, Gemma, or similar ecosystems.
Technical Qualifications:
- Proficient hands-on programming capabilities in Python, with a focus on building AI agents.
- Practical experience with LLMs, RAG, vector databases, embedding models, prompt engineering, and AI application development.
- Familiarity with one or more AI LLM frameworks including LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or similar tools.
- Knowledge of REST APIs, microservices, Git-based development, unit testing, and contemporary software engineering methodologies.
- Understanding of SQL and structured data principles such as tables, schemas, joins, relationships, and query formulation.