Full Stack Engineer

Job Title: Full-Stack Engineer (Python & Generative AI)

Role Overview

Full-Stack Engineer

chatbot interfaces, conversational agents, and data retrieval systems

Key Responsibilities

• AI Orchestration & Development:

  Design, implement, and maintain advanced

  RAG pipelines

  , including document ingestion, chunking strategies, semantic search, and vector database optimization.

• Backend Systems:

  Architect and build scalable, secure, and high-performance

  APIs and microservices

  using

  Python

  frameworks (e.g., FastAPI, Flask) to orchestrate LLM workflows and handle complex application logic.

• Frontend Engineering:

  Develop responsive, accessible, and intuitive UI components for

  chatbots, interactive dashboards, and AI copilots

  using modern frontend technologies (e.g., React, Next.js, or Vue.js).

• Agentic Frameworks:

  Implement

  agent-based architectures

  and multi-step conversational context management using orchestration tools like

  LangChain, LangGraph, or LlamaIndex

  .

• Performance & Reliability:

  Evaluate, version, and optimize

  prompt engineering templates

  to reduce hallucinations, cut system latency, and balance API runtime costs.

• Data & Infrastructure Management:

  Design schemas and optimize performance across relational databases (PostgreSQL/MySQL), NoSQL, and

  vector databases

  (e.g., Pinecone, Chroma, Milvus, Qdrant, PGVector).

Required Skills & Qualifications

1. Backend & AI Layer

• Core Language:

  Advanced proficiency in

  Python

  (async programming, multiprocessing, error handling, and object-oriented design).

• Frameworks:

  Hands-on experience with

  FastAPI, Flask, or Django

  .

• Generative AI & LLMs:

  Demonstrated experience integrating foundational models from major providers (OpenAI, Anthropic, Google, Azure) or hosting open-source variants (Llama, Mistral).

• RAG Expertise:

  In-depth knowledge of

  embeddings, hybrid search (BM25 + dense search), cross-encoders, reranking

  , and context window optimization.

2. Frontend Layer

• Languages & Tooling:

  Strong fundamentals in

  JavaScript/TypeScript, HTML5, and CSS3

  .

• Frameworks:

  Extensive experience with

  React.js, Next.js

  , or similar modern frameworks.

• State Management:

  Experience managing continuous state streaming (such as Server-Sent Events/WebSockets) for real-time chatbot response streaming.

3. DevOps, Cloud & Databases

• Databases:

  Comfort with both structured SQL and specialized

  vector stores

  .

• Cloud & Containers:

  Experience deploying AI applications on cloud providers (

  AWS, GCP, or Azure

  ) using

  Docker

  containers.

• CI/CD:

  Proficient with Git and automated testing/deployment workflows.

Nice-to-Have Skills

• Familiarity with LLM evaluation frameworks (e.g., Ragas, TruLens).
• Experience with fine-tuning open-source LLMs or parameter-efficient tuning techniques (LoRA/QLoRA).
• Familiarity with the Model Context Protocol (MCP) and tool-calling paradigms.
• Background in developing voice-enabled AI assistants or multimodal