Solutions Architect - Applied AI

Lyzr AI; Bengaluru; Hybrid; INR 1,500,000 - 3,000,000; Full time

Job summary

Lyzr AI is looking for an experienced AI Solutions Architect with a robust background in machine learning and natural language processing. This hybrid or remote position requires expertise in designing innovative AI agents and workflows, guiding model strategies, and leading development efforts to assure high-quality production solutions.

The ideal candidate will have at least 8 years of experience, including 3 years focused on ML and NLP, and 1 year with large language models. Strong stakeholder engagement skills are essential for success in this role.

Qualifications

• 8+ years of experience in relevant fields.
• 3-4 years of experience in ML and NLP.
• 1-2 years of experience with LLMs.

Responsibilities

• Design and implement AI agents and workflows.
• Guide model strategy and execution.
• Oversee data enrichment and intelligence.
• Lead development of production-grade solutions.

Skills

Machine Learning (ML)

Natural Language Processing (NLP)

Large Language Models (LLMs)

Cloud Platforms (AWS/GCP/Azure)

Agent Orchestration

Job description

Location: Bangalore/Hybrid or Remote

Experience: 8 years+

ML/Advanced NLP: 3-4 years

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.