Prompt Engineer at NomiSo

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

26 May, 26

Salary

0.0

Posted On

25 Feb, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Prompt Engineering, LLMs, Generative AI, Foundation Models, Retrieval Systems, RAG Pipelines, Vector Databases, Few-Shot Learning, Chain-Of-Thought Prompting, Python, Model Cost Optimization, LLM Evaluation, Guardrails, API Integration, Semantic Search, Cloud Platforms

Industry

Software Development

Description

About Company : Nomiso is a product and services engineering company. We are a team of Software Engineers, Architects, Managers, and Cloud Experts with expertise in Technology and Delivery Management. Our mission is to empower and enhance the lives of our customers through efficient solutions for their complex business problems. At Nomiso, we encourage entrepreneurial spirit — to learn, grow, and improve. A great workplace thrives on ideas and opportunities. That is a part of our DNA. We’re in pursuit of colleagues who share similar passions, are nimble, and thrive when challenged. We offer a positive, stimulating, and fun environment – with opportunities to grow, a fast-paced approach to innovation, and a place where your views are valued and encouraged. We invite you to push your boundaries and join us in fulfilling your career aspirations! Position Overview: We are looking for a highly skilled and experienced Prompt Engineer to design, optimize, and productionize prompts for Large Language Models (LLMs) and Generative AI systems. The ideal candidate will have deep experience working with foundation models, retrieval systems, and AI application architecture. You will collaborate closely with AI/ML engineers, data scientists, and product teams to design reliable, scalable, and business-ready GenAI solutions. This role requires both hands-on experimentation and architectural thinking — ensuring prompts are robust, cost-efficient, and aligned with real-world business use cases. Roles and Responsibilities: Design, test, and optimize prompts for LLM-based applications (chatbots, copilots, automation agents, summarization systems, etc.). Develop advanced prompting strategies including few-shot learning, chain-of-thought prompting, role prompting, and structured outputs. Build and optimize Retrieval-Augmented Generation (RAG) pipelines. Work with vector databases and embedding models for semantic search and contextual AI systems. Evaluate and benchmark LLM performance using structured evaluation metrics (hallucination rate, factual consistency, response quality). Collaborate with AI/ML and backend teams to integrate LLM solutions into production systems. Implement guardrails, safety filters, and prompt security strategies. Optimize token usage and model costs while maintaining output quality. Experiment with multiple LLM providers (OpenAI, Anthropic, open-source LLMs, etc.). Document prompt strategies, evaluation frameworks, and best practices. Mentor junior AI engineers in prompt engineering techniques. Stay updated with the latest advancements in Generative AI and LLM research. Must Have Skills: 8+ years of overall experience in software engineering, AI/ML, or related fields. 3+ years hands-on experience working with LLMs and Generative AI systems. Strong experience in Prompt Engineering for GPT-class or similar foundation models. Deep understanding of LLM architecture concepts (transformers, embeddings, attention mechanisms). Establish and manage the prompt lifecycle, including versioning, deployment, monitoring, and retirement of prompts to ensure consistency and performance at scale. Hands-on experience building RAG pipelines. Experience with vector databases (Pinecone, Weaviate, FAISS, etc.). Strong Python programming skills. Experience integrating LLMs into production applications (APIs, microservices). Focus on transforming unstructured data outputs from LLMs into reliable, structural outcomes (e.g., JSON, XML) for seamless consumption by downstream systems and applications. Familiarity with evaluation frameworks for GenAI systems. Strong understanding of model limitations, hallucination mitigation, and prompt guardrails. Experience working with cloud platforms (AWS preferred). Good to Have: Experience with LangChain / LlamaIndex / Semantic Kernel. Exposure to fine-tuning LLMs or parameter-efficient tuning techniques (LoRA, adapters). Experience with AI observability tools. Understanding of AI security and responsible AI practices. Experience with multi-agent systems and autonomous AI agents. Exposure to multimodal models (text + image). Qualification: Bachelor’s or Master’s degree in Computer Science Engineering, AI, Data Science, or related technical field. Location: Hyderabad, India Website: https://www.nomiso.io/ About Nomiso: Nomiso is a product and services engineering company. We are a team of Software Engineers, Architects, Managers, and Cloud Experts with expertise in Technology and Delivery Management. Our mission is to Empower and Enhance the lives of our customers, through efficient solutions for their complex business problems. At Nomiso we encourage entrepreneurial spirit - to learn, grow and improve. A great workplace, thrives on ideas and opportunities. That is a part of our DNA. We’re in pursuit of colleagues who share similar passions, are nimble and thrive when challenged. We offer a positive, stimulating and fun environment – with opportunities to grow, a fast-paced approach to innovation, and a place where your views are valued and encouraged. We invite you to push your boundaries and join us in fulfilling your career aspirations! We are an equal opportunity employer and are committed to diversity, equity, and inclusion. We do not discriminate on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other protected characteristics.

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Responsibilities

The Prompt Engineer will be responsible for designing, testing, and optimizing prompts for various LLM-based applications, developing advanced prompting strategies, and building/optimizing Retrieval-Augmented Generation (RAG) pipelines. This role also involves evaluating LLM performance, integrating solutions into production systems, and implementing necessary guardrails and cost optimizations.