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Onsite or remote • Ahmedabad+1💼 Full-time🗓 2026-06-25

Core

Design, develop, and deploy intelligent applications leveraging Large Language Models (LLMs) and advanced NLP techniques to solve real-world problems for clients.

Role type

Senior AI Engineer (LLM & NLP focus)

Builds

Intelligent applications integrating LLMs, RAG, and generative features

Domain

Artificial Intelligence / Natural Language Processing

Deliverable

production ML models

Required skills

Deep learning architectures, Transformer models (BERT, GPT, LLaMA), LLM fine-tuning, Prompt engineering, MLOps (CI/CD, monitoring), Cloud infrastructure (AWS, GCP, Azure), Model optimization and scaling

Preferred skills

LangChain, Hugging Face, PyTorch, TensorFlow, OpenAI APIs

Technologies

LangChain, Hugging Face Transformers, OpenAI APIs, PyTorch, TensorFlow, AWS, GCP, Azure

Responsibilities

Design and develop advanced AI/NLP solutions focusing on large language models; Implement and enhance NLP capabilities like text generation, summarization, and RAG; Craft effective prompts and fine-tune LLMs for specific domains; Deploy and scale models in production environments with MLOps best practices; Utilize cloud services for model training, deployment, and infrastructure management; Continuously optimize model performance and implement scaling strategies; Collaborate with data scientists, software engineers, and product managers to integrate AI capabilities; Stay up-to-date with latest AI research and experiment with new algorithms

Seniority

Senior, hands-on IC

Rewrite
## About the Role We are seeking an AI Engineer (Level 3) with a deep passion for exploring AI technologies, particularly Large Language Models (LLMs) and advanced NLP techniques. The ideal candidate will have a strong foundation in deep learning architectures and experience with state-of-the-art AI frameworks. In this role, you will work on developing and deploying intelligent applications that solve real-world problems for our clients, leveraging tools like LangChain, Hugging Face, and OpenAI APIs. ## Key Responsibilities - Design and Develop AI Solutions: Build advanced AI and NLP solutions focusing on large language models. Experiment with state-of-the-art Transformer architectures (e.g., BERT, GPT, LLaMA) to address complex language understanding and generation tasks. - LLM Implementation: Leverage modern AI frameworks and tools (such as LangChain, Hugging Face Transformers, OpenAI APIs, PyTorch, TensorFlow) to develop, fine-tune, and integrate LLMs into applications. Ensure models are optimized and efficient for production use. - Advanced NLP Techniques: Implement and enhance NLP capabilities like text generation, summarization, and retrieval-augmented generation (RAG) to provide intelligent and contextually aware features in our solutions. - Prompt Engineering & Fine-Tuning: Craft effective prompts and fine-tune LLMs to improve model performance for specific domains and use cases. Experiment with prompt strategies and training techniques to optimize generative models. - Production Deployment & MLOps: Deploy and scale models in production environments, ensuring reliability and low-latency performance. Implement MLOps best practices (CI/CD pipelines, model monitoring, version control) to achieve robust, repeatable model deployments. - Cloud AI Integration: Utilize AI/ML cloud services (AWS, GCP, Azure) for model training, deployment, and infrastructure management. Optimize cloud resources to handle large-scale training and inference efficiently. - Model Optimization & Scaling: Continuously optimize model performance and implement scaling strategies to handle large datasets and high-volume usage. - Cross-Functional Collaboration: Work closely with data scientists, software engineers, and product managers to integrate AI capabilities into products and solutions. Collaborate in cross-functional teams to gather requirements, brainstorm ideas, and ensure AI systems meet client needs and quality standards. - Innovation and Research: Stay up-to-date with the latest AI research, papers, and trends in the industry. Proactively experiment with new algorithms, models, and techniques to keep our solutions at the cutting edge. ## Required Qualifications - Education & Experience: Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent experience), plus 5+ years of hands-on experience in AI/ML engineering or a similar role. - Deep Learning & LLM Expertise: Strong foundation in deep learning and NLP, with experience in transformer-based architectures.
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