Context Engineer

Job Title: Context Engineer

Req ID: 58791

Job Function: Engineering

Posting Start Date: 26/05/2026

Posting End Date: 07/06/2026

Division: Digital

Job Location: IND-Bengaluru-RMZ Ecoworld

Advertised Salary: Competitive

Job Req ID: 58791

Posting Date: 26 May 2026

Location: Bengaluru

Salary: Competitive

About The Role

The Context Engineer is a specialist software engineering role focused on ensuring that AI systems receive the right information, in the right structure, at the right time to produce reliable, high‑quality outputs.

While similar in seniority to a Software Engineering Specialist, this role differs fundamentally in skillset and ways of working. Instead of writing application logic or services, the Context Engineer designs and engineers the context layer that powers AI agents—system prompts, tool definitions, memory strategies, and retrieval pipelines—across the software development lifecycle (SDLC).

This role is critical to making AI‑enabled delivery predictable, scalable, and safe.

What You’ll Be Doing

Context & Prompt Engineering (Core)
Design, implement, and maintain system prompts, developer prompts, and tool definitions that guide AI agents across different SDLC stages.
Treat prompts and context artefacts as versioned, testable engineering assets, not ad‑hoc instructions.
Ensure prompts are clear, precise, and aligned to desired outcomes, constraints, and quality standards.
Context Architecture & Schemas
Define context schemas tailored to specific AI agents and SDLC phases (e.g. design agents, code‑generation agents, code‑review agents, test‑generation agents).
Decide what information is required, optional, or excluded for each agent to maximise signal‑to‑noise ratio.
Collaborate with architects and engineers to ensure context aligns with system boundaries, patterns, and guardrails.
Context Window & Memory Management
Actively manage context window budgets, making informed decisions about:
what content is retained
what is summarised
what is evicted as conversations or agent loops grow
Design and maintain memory strategies (short‑term, long‑term, episodic, summarised) that support effective multi‑step reasoning.
Retrieval‑Augmented Generation (RAG)
Design and maintain retrieval pipelines that surface relevant knowledge to AI agents at runtime.
Own RAG components including:
embeddings strategies
vector databases
re‑ranking and filtering logic
Ensure retrieved context is accurate, relevant, and appropriately scoped for the task at hand.
Prompt Lifecycle, Testing & Optimisation
Own prompt versioning, change control, and rollback strategies.
Run A/B testing and evaluation of prompts and context strategies to measure impact on quality, speed, and reliability.
Continuously refine prompts and context structures based on observed outcomes and failure modes.
Knowledge Codification & Collaboration
Work closely with domain experts, engineers, and business stakeholders to codify institutional knowledge into AI‑consumable formats.
Transform tacit knowledge, guidelines, standards, and playbooks into structured, retrievable context.
Ensure knowledge remains current, governed, and reusable across teams and pods.
Governance, Risk & Responsible AI
Ensure context and prompts embed appropriate constraints, safety rules, and quality expectations.
Identify and mitigate risks such as hallucination, outdated knowledge, over‑broad context, or leakage of sensitive information.
Support auditability by maintaining clear lineage of prompts, context sources, and changes.

Essential Skills / Experience

Strong experience in prompt engineering and context design for LLM‑based systems.
Solid understanding of information architecture and structuring complex knowledge.
Hands‑on experience with RAG stacks, including vector databases, embeddings, and re‑ranking approaches.
Deep understanding of token‑level reasoning, context windows, and model constraints.
Exceptional written communication skills—clear, precise, and unambiguous writing is critical.
Background in software engineering, data engineering, or platform engineering.
Experience working with AI agents across different SDLC phases.
Familiarity with evaluation frameworks for LLM outputs.
Experience operating in regulated or enterprise environments.

BT Group is the UK’s leading communications group and the holding company behind some of the country’s most recognised brands – including BT, EE, Openreach and Plusnet. Our purpose is as simple as it is ambitious: we connect for good. Our customers include consumers, small, medium and large businesses, public sector organisations and other communications providers.

BT Group’s role is about setting direction, unlocking value and creating the conditions for our brands and businesses to thrive.

Having come through the most capital-intensive phase of our fibre investment, our focus now is on what comes next – simplifying how we operate, using technology and AI to work smarter, and organising ourselves to serve customers better and grow sustainably. Group teams shape strategy, policy, brand, capital allocation and transformation, helping the whole organisation perform at its best.

We have a singular culture that unites all our people: we are customer-first challengers, who are committed, clear and connected. These behaviours unite us as one team to deliver for our colleagues, our customers, our stakeholders and the country. Joining BT Group means working at the heart of a business that matters to the UK, with the opportunity to shape decisions, influence outcomes and help set the future course of one of the country’s most important companies.