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 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. 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 solid 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.

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.

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.

📌 Gen AI Expert (Hyderabad)
🏢 Nomiso
📍 Hyderabad