GenAI Expert

at Nomiso

GenAI Expert

Nomiso

Company

Home

GenAI Expert at Nomiso · Bengaluru (Bangalore) · 6 - 10 years · Bootstrapped · Posted 4 Nov 2026

GenAI Expert

at Nomiso

Posted by Varnika Tyagi

6 - 10 yrs

Best in industry

Bengaluru (Bangalore)

Skills

Huggingface

OpenAI API

Software Testing (QA)

Generative AI

Machine Learning (ML)

Large Language Models (LLM)

Retrieval Augmented Generation (RAG)

We are seeking a senior Generative AI Systems Expert with 8+ years of deep engineering experience to architect production-grade AI systems (RAG pipelines, fine-tuning, prompt optimization, inference scaling). This is a specialized Individual Contributor (IC) role for a production-focused builder who owns the AI implementation end-to-end—from initial architecture to deployment, optimization, and evaluation.

While your primary mastery lies in building and scaling robust LLM applications, you possess a strong, rigorous engineering mindset. Experience or strong familiarity with Quality Assurance (QA), Test Automation, and AI evaluation frameworks is highly preferred to ensure our systems behave reliably and deterministically at scale. You are not just implementing off-the-shelf models; you are designing complex retrieval systems, optimizing inference pipelines, evaluating model trade-offs, and building systems that reliably serve millions of transactions.

🎯 What We’re Looking For (Who You Are)

AI Systems Architect (Core): 4+ years of dedicated experience building and shipping LLM systems in production. You understand model selection, RAG architecture, fine-tuning strategies, inference optimization, and cost management. You make pragmatic trade-offs between accuracy, latency, and cost.
Production-Focused Builder: You've shipped LLM systems at scale. You are deeply comfortable with tokenization, context windows, vector database optimization, handling hallucinations, and system failures.
Independent Operator: You own AI features end-to-end—from problem definition through evaluation, deployment, and production monitoring. You unblock yourself and make good decisions with incomplete information.
Quality & Evaluation-Minded (Highly Valued Plus): You possess a rigorous engineering background with a strong appreciation for testing. You understand how to handle non-deterministic outputs through deterministic test design, evaluate model performance (F1, precision, recall), and build/use automation frameworks to prevent regressions.
Team Amplifier: You mentor engineers on AI best practices, participate in code reviews, and help scale GenAI adoption across the organization.
Continuous Experimenter: You A/B test approaches, iterate based on production data, and stay current with the rapid evolution of LLMs.

🛠️ Technical Stack & Qualifications

GenAI Core & Architecture (Must-Have)

LLM Ecosystem: Deep experience with LLM APIs (OpenAI, Anthropic, Hugging Face), model trade-offs, and selection.
Retrieval: RAG pipeline architecture and vector databases (Pinecone, Weaviate, FAISS).
Optimization: Prompt engineering, fine-tuning (PEFT/LoRA), and inference optimization.
Operations: Cost/latency management, hallucination detection, and mitigation.
Advanced Patterns: Agentic patterns, multi-step reasoning, and building for reliability under uncertainty.
Education: Degree in Computer Science, Mathematics, or equivalent professional experience. A practical portfolio demonstrating GenAI/ML implementation is valued highly.

Testing & Automation (Highly Desirable / Good-to-Have)

Domain Expertise: 5+ years in the QA/Testing domain.
AI-Specific Validation: Regression testing for models, hallucination testing, bias detection, and mocking LLM APIs.
Deterministic Design: Handling non-deterministic AI outputs through deterministic test frameworks.
Automation Suite: Proficiency in test case design (positive, negative, edge cases) and frameworks like pytest, Jest, Selenium, or Cypress.
CI/CD: Experience integrating automated test execution into CI/CD pipelines.

💼 Key Responsibilities

Build: Design and implement end-to-end GenAI systems—RAG pipelines, fine-tuning strategies, and prompt optimization. Make pragmatic choices between model options, latency/cost trade-offs, and production constraints.
Ship & Guard: Own the deployment of AI features. Architect the system to ensure reliability, scalability, and proper handling of edge cases (hallucinations, rate limits, failures) using robust validation strategies.
Measure & Evaluate: Instrument systems with telemetry. Run A/B experiments comparing AI approaches. Establish metrics dashboards to track both system performance and model accuracy/alignment over time.
Collaborate: Mentor engineers on AI best practices. Participate in code reviews. Help the team adopt and scale GenAI capabilities safely and effectively.

🎯 What We’re Looking For (Who You Are)

AI Systems Architect (Core): 4+ years of dedicated experience building and shipping LLM systems in production. You understand model selection, RAG architecture, fine-tuning strategies, inference optimization, and cost management. You make pragmatic trade-offs between accuracy, latency, and cost.
Production-Focused Builder: You've shipped LLM systems at scale. You are deeply comfortable with tokenization, context windows, vector database optimization, handling hallucinations, and system failures.
Independent Operator: You own AI features end-to-end—from problem definition through evaluation, deployment, and production monitoring. You unblock yourself and make good decisions with incomplete information.
Quality & Evaluation-Minded (Highly Valued Plus): You possess a rigorous engineering background with a strong appreciation for testing. You understand how to handle non-deterministic outputs through deterministic test design, evaluate model performance (F1, precision, recall), and build/use automation frameworks to prevent regressions.
Team Amplifier: You mentor engineers on AI best practices, participate in code reviews, and help scale GenAI adoption across the organization.
Continuous Experimenter: You A/B test approaches, iterate based on production data, and stay current with the rapid evolution of LLMs.

🛠️ Technical Stack & Qualifications

GenAI Core & Architecture (Must-Have)

LLM Ecosystem: Deep experience with LLM APIs (OpenAI, Anthropic, Hugging Face), model trade-offs, and selection.
Retrieval: RAG pipeline architecture and vector databases (Pinecone, Weaviate, FAISS).
Optimization: Prompt engineering, fine-tuning (PEFT/LoRA), and inference optimization.
Operations: Cost/latency management, hallucination detection, and mitigation.
Advanced Patterns: Agentic patterns, multi-step reasoning, and building for reliability under uncertainty.
Education: Degree in Computer Science, Mathematics, or equivalent professional experience. A practical portfolio demonstrating GenAI/ML implementation is valued highly.

Testing & Automation (Highly Desirable / Good-to-Have)

Domain Expertise: 5+ years in the QA/Testing domain.
AI-Specific Validation: Regression testing for models, hallucination testing, bias detection, and mocking LLM APIs.
Deterministic Design: Handling non-deterministic AI outputs through deterministic test frameworks.
Automation Suite: Proficiency in test case design (positive, negative, edge cases) and frameworks like pytest, Jest, Selenium, or Cypress.
CI/CD: Experience integrating automated test execution into CI/CD pipelines.

💼 Key Responsibilities

Build: Design and implement end-to-end GenAI systems—RAG pipelines, fine-tuning strategies, and prompt optimization. Make pragmatic choices between model options, latency/cost trade-offs, and production constraints.
Ship & Guard: Own the deployment of AI features. Architect the system to ensure reliability, scalability, and proper handling of edge cases (hallucinations, rate limits, failures) using robust validation strategies.
Measure & Evaluate: Instrument systems with telemetry. Run A/B experiments comparing AI approaches. Establish metrics dashboards to track both system performance and model accuracy/alignment over time.
Collaborate: Mentor engineers on AI best practices. Participate in code reviews. Help the team adopt and scale GenAI capabilities safely and effectively.

Users love Cutshort

Read about what our users have to say about finding their next opportunity on Cutshort.

Shubham Vishwakarma

Full Stack Developer - Averlon

I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.

Companies hiring on Cutshort

About Nomiso

Founded :

2021

Type :

Services

Size :

20-100

Stage :

Bootstrapped

About

We are Nomiso a software co-engineering company and your partner in solving complex business problems with technology. We collaborate with our clients to understand business- and industry-specific challenges and engage with them to push the boundaries of what's possible to deliver impact at pace and scale. At Nomiso, we are not just delivering innovative solutions; we are Co-engineering Excellence.

Similar jobs (10)

AI Implementation Engineer

at Wissen Technology

4 recruiters

Posted by Anishka Burde

Mumbai

3 - 13 yrs

Best in industry

Java

Python

LangGraph

LangChain

Retrieval Augmented Generation (RAG)

+2 more

Job Summary:

Wissen Technology is hiring an AI Implementation Engineer to build, deploy, and scale enterprise-grade Generative AI solutions across business-critical applications. The role involves developing production-ready AI systems using Azure AI services, Large Language Models (LLMs), RAG architectures, and agent-based frameworks while collaborating closely with engineering teams to drive AI adoption and innovation.

Experience

6-12 years

Location

Mumbai / Bangalore

Mode of Work

Hybrid

Mandatory Skills (Must Have)

• Python programming (6+ years) including asynchronous programming and backend application development

• Java and Spring Framework (3+ years) for enterprise-scale application integration

• Azure OpenAI Service, Azure AI Foundry, and Azure AI Search for production GenAI applications

• Retrieval Augmented Generation (RAG) architecture including embeddings, chunking, vector databases, reranking, and grounding techniques

• Agent Frameworks such as Microsoft Agent Framework, Semantic Kernel, AutoGen, LangChain, or LangGraph

• Snowflake and Cortex AI (Cortex Search, LLM Functions) with strong SQL expertise

• Prompt Engineering, LLM evaluation frameworks, testing, and model performance optimization

• DevOps and Cloud Deployment using Azure DevOps, GitHub Actions, Docker, AKS, Azure Functions, and observability tools

Optional Skills (Good to Have)

• React.js for AI-powered user interfaces and conversational applications

• Azure AI Content Safety and Responsible AI implementation experience

• Financial Services, Banking, or other regulated industry domain experience

• Real-time streaming applications and token-level LLM operations

• Performance optimization, caching strategies, and cost optimization for AI workloads

• Microsoft Azure AI Engineer Associate Certification