Solutions Architect - Applied AI

Solutions Architect - Applied AI Shape enterprise AI with multi-agent systems, LLMs, and NLP — own architecture, data strategy, and production-ready solutions that deliver impact. Location: Bangalore/Hybrid or Remote Experience: 8 years+ Experience in ML/Advanced NLP: 3-4 years Experience in LLMs - 1-2 years Employment Type: Full-Time About the Role We are seeking an experienced and innovative AI Solutions Architect to join our team. This role combines deep technical expertise in machine learning, large language models (LLMs), and NLP with consultative skills to help design, guide, and implement cutting-edge AI agents and workflows. You will play a critical role in determining which AI agents should be built, how data should be handled, and how models should be integrated and optimized to deliver intelligent and reliable outcomes. Key Responsibilities Agent Architecture & Workflow Design Consult with internal and client teams to determine which agent workflows need to be created for specific use cases. Define capabilities, intents, and interaction flows of AI agents to meet business goals. Design the system architecture for multi-agent orchestration and model integration. Model Strategy & Execution Guide the selection, fine-tuning, and integration of LLMs, RAG pipelines, and transformer-based models. Define how models will interact with different data modalities (text, audio, video, structured data). Evaluate and benchmark model performance and retraining needs. Data Enrichment & Intelligence Advise on how to configure, enrich, and pre-process data (structured/unstructured) for maximum model performance. Oversee entity extraction, topic modeling, summarization, and other NLP-driven enrichment techniques. Ensure data pipelines are designed for continuous learning and improvement. Solution Implementation & Code Ownership Lead prototyping and MVP development, translating architecture into production-grade solutions. Collaborate with developers to build scalable AI services and interfaces. Write clean, efficient, and modular code to integrate AI components. Stakeholder Engagement & Technical Consultation Translate complex AI concepts into actionable recommendations for non-technical stakeholders. Provide strategic input on product direction, capabilities, and limitations. Maintain a high degree of ownership and accountability across all initiatives. Required Skills & Experience Core AI/ML Expertise (3–4 years) Strong background in machine learning and deep learning, including model training and evaluation. Experience developing ML-powered enterprise applications. NLP & Data Intelligence (2–3 years) Advanced understanding of NLP techniques, including entity extraction, summarization, topic modeling, etc. Experience with structured and unstructured data feature engineering. LLM & Agent Ecosystem (1–2 years) Solid understanding of LLM architecture, prompt engineering, and RAG (Retrieval-Augmented Generation) frameworks. Exposure to multi-modal agent delivery (text, audio, video). Additional Qualities Strong problem-solving aptitude applied to real-world use cases. Excellent communication and stakeholder management skills. Proactive mindset with ownership of end-to-end delivery. Preferred Qualifications Experience with cloud-based ML platforms (AWS/GCP/Azure). Familiarity with agent orchestration platforms or LangChain-like frameworks. Understanding of vector databases, embeddings, and context-aware retrieval systems. Department Applied AI Locations Bengaluru