AI prompt engineers design, test, and optimize prompts and evaluation workflows that improve large language model performance in real production pipelines. On Rex.zone, these remote full-time roles focus on prompt engineering, RLHF-style preference data, prompt evaluation, QA evaluation, and safety alignment for NLP assistants, agentic tools, and enterprise copilots. You will collaborate with engineers and researchers to write system prompts, build prompt libraries, run A/B tests, define rubrics, and measure model performance improvement across accuracy, helpfulness, and content safety. Explore prompt engineer jobs in India with global teams, clear deliverables, and measurable impact on LLM training pipelines.
Title: AI Prompt Engineer Jobs in India Date: 25-02-2026 Company: Rexzone Country: US Remote Type: Remote Employment Type: FULL_TIME Experience Level: Mid-Senior Industry: Technology Job Function: Engineering Skills: Prompt Engineering, LLM Evaluation, RLHF, Prompt Evaluation, QA Evaluation, NLP, Instruction Tuning, Safety Alignment, RAG, Python Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR
You will craft and iterate high-performing prompts for LLM-based applications, build reusable prompt templates, and define evaluation criteria that improve reliability, groundedness, and safety. You will run prompt experiments (A/B tests), analyze outputs, create preference/ranking data for RLHF-style training, and partner with engineering to integrate prompts into products (chatbots, copilots, agents, and RAG systems). Your work supports model performance improvement, training data quality, and consistent behavior across domains such as customer support, coding help, research summarization, and enterprise knowledge search.
Own prompt lifecycle management including prompt writing, prompt debugging, prompt versioning, and prompt library maintenance. Design prompt evaluation plans using rubrics for accuracy, completeness, instruction following, tone, and policy compliance. Produce high-quality preference data and rationales to support RLHF and instruction tuning workflows. Collaborate with stakeholders to translate product requirements into system prompts, developer prompts, and user prompt patterns. Improve grounding and reduce hallucinations using retrieval-augmented generation (RAG) strategies and citation/traceability constraints. Create and maintain annotation guidelines compliance for evaluators, including edge-case handling and escalation paths. Monitor quality metrics such as pass rate, inter-annotator agreement, and regression failures across releases. Support content safety labeling and red-teaming prompts to prevent policy violations, sensitive data leakage, and jailbreak behaviors.
3+ years in software engineering, ML/NLP workflows, or applied LLM prompt engineering. Strong writing precision for structured instructions, role prompts, and multi-step reasoning constraints. Experience with LLM evaluation methods such as rubrics, pairwise ranking, golden sets, and adversarial testing. Familiarity with RLHF concepts, preference data, and human feedback loops. Ability to analyze qualitative and quantitative results to drive iterative prompt improvements. Working knowledge of Python and basic tooling for experimentation (notebooks, scripts, JSON, APIs).
Experience with RAG pipelines, vector search, embedding evaluation, and grounded answer validation. Knowledge of content safety labeling, policy taxonomy, and prompt-based guardrails. Exposure to named entity recognition, information extraction, or domain-specific ontologies for structured outputs. Familiarity with LLM observability and evaluation tooling (unit tests for prompts, regression suites, eval harnesses). Experience collaborating with annotation vendors or BPO teams and managing QA evaluation workflows at scale.
Prompt libraries for consistent instruction following across multiple tasks. Evaluation rubrics and benchmark sets to measure quality and detect regressions. RLHF-style preference datasets with clear rationales and consistent scoring. Safety test suites for jailbreak resistance and policy compliance. Structured-output prompts for JSON schemas, tool calling, and agent orchestration. RAG prompts that enforce grounding, citations, and uncertainty handling.
This posting targets AI prompt engineer jobs in India and covers common modifiers and related domains: remote, full-time, contract, freelance, entry-level, senior; NLP, content safety, LLM training pipelines, QA evaluation, prompt evaluation, RLHF, instruction tuning, data labeling, and model performance improvement. Suitable for candidates from AI labs, tech startups, enterprises, BPOs, and annotation vendors working on LLM assistants and agentic workflows via Rex.zone.
Apply through Rex.zone by submitting your resume and a short portfolio showing prompt examples, evaluation rubrics, or prompt experiment results. Include links to any relevant work such as prompt libraries, LLM evaluation reports, RAG prototypes, or safety test cases. Applications are reviewed for prompt clarity, evaluation rigor, and ability to improve training data quality and end-user reliability.
They design and optimize prompts, build prompt templates and system instructions, run prompt evaluation and A/B tests, and create feedback data (often RLHF-style preference data) to improve model reliability, safety, and instruction following in real applications.
Yes. These roles are explicitly marked Remote and are designed for distributed collaboration with global teams while targeting candidates based in India.
Prompt engineering, LLM evaluation, RLHF concepts, prompt evaluation, QA evaluation, strong structured writing, NLP fundamentals, safety alignment, and practical experience with RAG and basic Python tooling.
Typical metrics include task success rate, rubric scores, regression test pass rates, reduced hallucination rates, improved groundedness, user satisfaction signals, and consistency across edge cases and safety constraints.
Often yes. Prompt engineers may define annotation guidelines, generate evaluation datasets, support preference labeling for RLHF, and coordinate QA evaluation to ensure training data quality.
NLP assistants, content safety, customer support automation, coding copilots, enterprise search with RAG, structured extraction (including named entity recognition), and agent/tool-calling workflows.
This specific posting is Full-Time and Mid-Senior, but the page covers common search modifiers including entry-level, senior, contract, and freelance to match how candidates search for prompt engineer roles.
Include before/after prompt iterations, evaluation rubrics, benchmark or golden sets, examples of safety test prompts, RAG prompting patterns, and brief notes showing how changes led to measurable model performance improvement.
'%20stroke-width='1.1184'/%3e%3cline%20x1='13.9799'%20y1='14.5392'%20x2='13.9799'%20y2='0.000106738'%20stroke='url(%23paint1_linear_5108_6545)'%20stroke-width='1.1184'/%3e%3cline%20y1='-0.559198'%20x2='29.0783'%20y2='-0.559198'%20transform='matrix(-1%208.74228e-08%208.74228e-08%201%2029.0781%20136.539)'%20stroke='url(%23paint2_linear_5108_6545)'%20stroke-width='1.1184'/%3e%3cline%20y1='-0.559198'%20x2='14.5391'%20y2='-0.559198'%20transform='matrix(1.31134e-07%201%201%20-1.31134e-07%2014.5391%20122)'%20stroke='url(%23paint3_linear_5108_6545)'%20stroke-width='1.1184'/%3e%3cdefs%3e%3clinearGradient%20id='paint0_linear_5108_6545'%20x1='29.0781'%20y1='-0.499878'%20x2='-0.000152544'%20y2='-0.49988'%20gradientUnits='userSpaceOnUse'%3e%3cstop%20stop-color='%23666666'/%3e%3cstop%20offset='0.336538'%20stop-color='white'/%3e%3cstop%20offset='0.495192'%20stop-color='white'/%3e%3cstop%20offset='0.653846'%20stop-color='white'/%3e%3cstop%20offset='1'%20stop-color='%23666666'/%3e%3c/linearGradient%3e%3clinearGradient%20id='paint1_linear_5108_6545'%20x1='15.0391'%20y1='14.5392'%20x2='15.0391'%20y2='0.000106877'%20gradientUnits='userSpaceOnUse'%3e%3cstop%20stop-color='%23585858'/%3e%3cstop%20offset='0.745192'%20stop-color='white'/%3e%3cstop%20offset='1'%20stop-color='white'/%3e%3c/linearGradient%3e%3clinearGradient%20id='paint2_linear_5108_6545'%20x1='0'%20y1='0.5'%20x2='29.0783'%20y2='0.5'%20gradientUnits='userSpaceOnUse'%3e%3cstop%20stop-color='%23666666'/%3e%3cstop%20offset='0.336538'%20stop-color='white'/%3e%3cstop%20offset='0.495192'%20stop-color='white'/%3e%3cstop%20offset='0.653846'%20stop-color='white'/%3e%3cstop%20offset='1'%20stop-color='%23666666'/%3e%3c/linearGradient%3e%3clinearGradient%20id='paint3_linear_5108_6545'%20x1='0'%20y1='0.5'%20x2='14.5391'%20y2='0.5'%20gradientUnits='userSpaceOnUse'%3e%3cstop%20stop-color='%23585858'/%3e%3cstop%20offset='0.745192'%20stop-color='white'/%3e%3cstop%20offset='1'%20stop-color='white'/%3e%3c/linearGradient%3e%3c/defs%3e%3c/svg%3e)
We believe exceptional intelligence deserves exceptional pay. Our platform consistently offers rates above the industry average, rewarding experts for their true value and real impact on frontier AI. Here, your expertise isn't just appreciated—it's properly compensated.
No office. No commute. No constraints. Our fully remote workflow gives experts complete flexibility to work at their own pace, from any country, any time zone. You focus on meaningful tasks—we handle the rest.
AI trainers are the heart of our company. We treat every expert with trust, humanity, and genuine appreciation. From personalized support to transparent communication, we build long-term relationships rooted in respect and care.
Ready to Shape the Future of AI Prompt Engineering?
Apply Now.