Location: Remote

Experience Level: 3-5+ years of overall engineering experience, including at least 1 year working with LLM-based agentic systems

About Us

We’re building a next-generation platform that leverages Large Language Models (LLMs) and agentic workflows to transform document processing, analytics, and decision-making for large interprises (Top Fortune 1000) and top CPA Firms. Our fast-growing team is looking for an AI Engineer who will design and implement robust agent flows, integrate advanced LLM frameworks and design patterns, and optimize context-building from multiple data sources.

What You’ll Do

• Design & Implement Agentic Workflows
  • Build AI “agents” with memory modules, context-building, and tool integration.
  • Develop strategies for connecting diverse data sources (e.g., DB, Vector Stores, other MCPs) into agent pipelines.
• LLM Integration & Prompt Engineering
  • Craft effective prompts for chain-of-thoughts for domain-specific tasks (extraction, reasoning, summarization, data augmentation) to reduce hallucinations.
  • Evaluate and fine-tune OpenSource LLM like LLaMA models for specific business needs.
• Architecture & Best Practices
  • Drive architecture design patterns for robust, scalable AI microservices.
  • Apply best practices in software engineering (CI/CD, testing, code reviews).
• Data Pipelines & Context Handling
  • Build/maintain data workflows for ingesting and storing large volumes of unstructured/structured data.
  • Collaborate with data team to ensure high-quality data is fed into LLM pipelines.
• Collaborate Across Teams
  • Work with clients, analyst, product, backend, and devops teams to bring agentic solutions into production.
  • Share your expertise in design and optimization of AI-driven applications.

Must-Have Experience

✅ 3**+ years** of software engineering experience

✅ 1+ year of direct experience building LLM-based agentic systems (memory, context-building, tool integration).

✅ Proven track record of prompt engineering and reducing hallucinations in LLM tasks.

✅ Experience with advanced retrieval-augmented generation (RAG) methods like vector databases, knowledge graphs, etc.

✅ Experience building scalable backend systems from scratch / 0-1 (not just maintenance).

✅ Strong understanding of design patterns (both traditional architecture and agentic flow).

✅ Hands-on approach - must verify work through unit testing & end-to-end evals.