Position Overview

We are looking for a highly skilled LLM/Prompt-Context Engineer with a strong fullstack Python background to design, develop, and integrate intelligent systems focused on large language models (LLMs), prompt engineering, and advanced context management. In this role, you will play a critical part in architecting context-rich AI solutions, crafting effective prompts, and ensuring seamless agent interactions using frameworks like LangGraph.

Key Responsibilities

Prompt & Context Engineering

Design, optimize, and evaluate prompts for LLMs to achieve precise, reliable, and contextually relevant outputs across a variety of use cases.

Context Management

Architect and implement dynamic context management strategies, including session memory, retrieval-augmented generation, and user personalization, to enhance agent performance.

LLM Integration

Integrate, fine-tune, and orchestrate LLMs within Python-based applications, leveraging APIs and custom pipelines for scalable deployment.

LangGraph & Agent Flows

Build and manage complex conversational and agent workflows using the LangGraph framework to support multi-agent or multi-step solutions.

Fullstack Development

Develop robust backend services, APIs, and (optionally) front-end interfaces to enable end-to-end AI-powered applications.

Collaboration

Work closely with product, data science, and engineering teams to define requirements, run prompt experiments, and iterate quickly on solutions.

Evaluation & Optimization

Implement testing, monitoring, and evaluation pipelines to continuously improve prompt effectiveness and context handling.

Required Skills & Qualifications

Deep experience with fullstack Python development (FastAPI, Flask, Django; SQL/NoSQL databases).

Demonstrated expertise in prompt engineering for LLMs (e.g., OpenAI, Anthropic, open-source LLMs).

Strong understanding of context engineering, including session management, vector search, and knowledge retrieval strategies.

Hands-on experience integrating AI agents and LLMs into production systems.

Proficient with conversational flow frameworks such as LangGraph.

Familiarity with cloud infrastructure, containerization (Docker), and CI/CD practices.

Exceptional analytical, problem-solving, and communication skills.

Preferred

Experience evaluating and fine-tuning LLMs or working with RAG architectures.

Background in information retrieval, search, or knowledge management systems.

Contributions to open-source LLM, agent, or prompt engineering projects.

Remote

About the Company

TechnoSphere, Inc.

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LLM/Prompt-Context Engineer – Fullstack Python (AI Agents, LangGraph, Context Engineering) @ Anand Rudra