Senior Generative Ai Engineer

Job Title: Senior Generative AI Engineer (Drafting & RAG Systems)

Role Overview

We are looking for a Senior Generative AI Engineer to lead the development and deployment of our next-generation Automated Drafting Tool. You will be responsible for the entire lifecycle of the AI features—from local prototyping using Ollama to scaling globally via OpenAI APIs.

The ideal candidate has a "Full-Stack AI" mindset: you understand how to retrieve context using RAG, manage high-dimensional data in Vector Databases, and ensure the final drafted output is coherent, accurate, and contextually aware.

Key Responsibilities

1. AI Architecture & Drafting Logic

• Design and implement end-to-end Retrieval-Augmented Generation (RAG) pipelines specifically optimized for document drafting.
• Develop advanced Prompt Engineering strategies to handle complex drafting constraints (tone, legal/technical compliance, and formatting).
• Implement hybrid model strategies, utilizing Ollama for local development, testing, and privacy-sensitive tasks, while orchestrating OpenAI (GPT-4o/o1) for production-level reasoning.

2. Data & Vector Engineering

• Build and maintain scalable Vector Databases (e.G., Pinecone, Weaviate, Milvus, or FAISS).
• Optimize document ingestion pipelines: chunking strategies, embedding model selection, and metadata filtering to improve retrieval precision.
• Implement "Agentic RAG" where the system can self-correct or multi-step reason through a draft.

3. Deployment & MLOps (Local to Cloud)

• Bridge the gap between local ideation (running models on Ollama/Local GPUs) and cloud production environments.
• Deploy AI services using containerization (Docker/Kubernetes) and manage API latency, rate limits, and token costs.
• Establish monitoring for AI performance, including hallucination detection and "groundedness" metrics.

Required Skills & Qualifications

Mandatory Experience

• Experience: 3+ years of professional experience in AI/Machine Learning or Backend Engineering with a heavy GenAI focus.
• LLM Orchestration: Deep hands-on experience with LangChain or LlamaIndex.
• Model Proficiency: Expert knowledge of the OpenAI API ecosystem and local model runners like Ollama.
• Vector Expertise: Proven track record of implementing and optimizing Vector Databases and RAG workflows.
• Programming: Mastery of Python (FastAPI/Flask) and asynchronous programming
• JIRA + Confluence exposure is must have

Technical Stack

• Models: OpenAI (GPT-4), Ollama (Llama 3, Mistral, Mixtral).
• Tools: LangChain, LlamaIndex, LangSmith (for tracing).
• Database: Pinecone, ChromaDB, or pgvector.
• Infrastructure: Docker, AWS/GCP/Azure, GitHub Actions for CI/CD.

What We Look For (The "Hacker" Mindset)

• Production Proven: You have moved at least one GenAI product from a Jupyter Notebook/Local Script to a live environment with real users.
• Problem Solver: You know how to handle the "stochastic" nature of LLMs and can build guardrails to prevent hallucinations in drafting.
• Architecture First: You care about token optimization and latency just as much as you care about the quality of the text generated.