Gen AI Developer

Job Description

Job Description

HubSync is looking for a Generative AI Developer to be a strategic partner to product, a hands-on technical lead, and a core driver of what we build and how we build it. If you are excited about designing and deploying GenAI systems from 0 to 1, and enjoy working in a fast-paced, high-impact environment, this role is for you.

Responsibilities

Partner closely with product leadership to translate early product ideas into working prototypes and then into robust, production-ready generative AI applications.

Design and build full-stack AI systems leveraging state-of-the-art large language models (LLMs) such as GPT-4, Claude, Mistral, LLaMA and similar, along with modern agentic frameworks (for example, LangGraph and CrewAI).

Own the 0-to-1 architecture for retrieval-augmented generation (RAG), agentic workflows, and document intelligence pipelines, including retrieval, orchestration, prompting, and output handling.

Experiment, build, and iterate rapidly, deploying MVPs to users and evolving solutions based on real-world feedback and performance data.

Build intelligent document processing (IDP) solutions to extract, understand, and generate insights from complex, unstructured documents.

Evaluate and benchmark models by testing different model providers, prompts, and configurations to optimize for quality, latency, cost, and reliability across target tasks.

Help shape our overall AI strategy and technology roadmap by staying current on emerging AI capabilities and identifying high-potential tools and approaches for early adoption.

What We’re Looking For (Requirements)

Someone collaborative and comfortable working in a fast-paced, startup-like environment with evolving priorities.

Hands-on experience building generative AI products that have meaningful, real user-facing impact.

Deep understanding of LLMs, prompt engineering, RAG techniques, and agentic workflow patterns.

Familiarity with tools such as LangChain, LlamaIndex, Hugging Face, and vector databases like Pinecone, Weaviate, or FAISS.

Experience working with LLMs via APIs (for example, OpenAI and Anthropic) and / or deploying and operating open-source models.

Strong software engineering best practices; you write clean, maintainable, production-grade code and are comfortable with modern development workflows.

Comfort working with AWS cloud infrastructure for deploying and scaling AI-powered applications.

Bonus Qualifications

Experience with intelligent document processing (IDP), form understanding, or document-based retrieval systems.

Prior work with agentic frameworks such as LangGraph, CrewAI, or AutoGen.

Knowledge of NLP techniques, embeddings, and LLM evaluation metrics to systematically assess and improve model performance.

Background in applied ML / AI research or AI product development at an early-stage company or similar high-growth environment.

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