AI/ML Solution Engineer

Role Overview

You will join the AI engineering team to help design, build and deploy agentic AI workflows and generative AI applications. You’ll work with LLMs, retrieval-augmented generation (RAG), vector databases, tool-calling agents (via n8n or other orchestration), and integrate them into production systems. Expect to learn fast, contribute small modules early, and ramp up into owning components.

Responsibilities

• Develop and integrate generative AI models and workflows: fine-tune LLMs, build prompt chains, deploy APIs.
• Build agentic workflows: multi-agent orchestration, tool invocation, memory/knowledge management, context handling.
• Use automation/orchestration tools (e.g., n8n, Airflow, Prefect) to connect agents, external services/APIs, and data flows.
• Construct RAG pipelines & vector search: embed documents, build retrieval systems, integrate with LLMs.
• Collaborate with product/data/backend teams to build scalable, reliable AI systems.
• Monitor and optimize performance: latency, cost, accuracy, throughput.
• Stay current with new generative/agentic frameworks and incorporate them.

Requirements

• Bachelor’s in Computer Science/Engineering or equivalent.
• ~1-2 years of experience in software/AI engineering.
• Programming proficiency in Python.
• Basic experience or strong interest in LLMs, prompt engineering, Generative AI.
• Familiarity or willingness to learn agentic/automation tooling (n8n, LangChain, Autogen, etc.).
• Understanding of or interest in vector databases/RAG (Pinecone, Chroma, Qdrant) and retrieval workflows.
• Good communication skills and ability to learn in a fast-paced environment.

Skills

• Python
• LLMs
• n8n
• LangChain
• RAG

Nice to Have

• Prior hands-on project with LLMs or generative AI in a startup/product setting.
• Exposure to orchestration/automation tools like n8n, Airflow, Prefect or low-code workflows.
• Experience with cloud platforms (AWS/GCP/Azure) and containerization (Docker).
• Experience with frontend/back-end integration (e.g., FastAPI, Flask).
• Knowledge of agentic frameworks, multi-agent coordination, memory management in agents.

Benefits

• Health insurance
• Paid sick time
• Paid time off
• Provident Fund