AI Architect

Role & responsibilities

Mandatory skll-

• Deep experience in Natural Language Processing (NLP and NLI), tiny/small language models, and base language model retraining (both transformer and BERT-based architectures).
• Extensive experience in conversational NLP systems.
• Experience in SLM adaptor fine-tuning, SFT training, and SLM pruning (base and instruct models).
• Experience in retriever frameworks in Q and A bots.
• Experience in contrastive learning and graph learning-based embedding methods with heterogeneous features.
• Preferably experience in building NLP active learning pipelines.
• Demonstrable experience in scaling your models to large-scale search systems is a plus.
• Very good critical reasoning skills, and data-centric NLP/NLI systems.
• Must have the temperament and hunger to work in a fast-paced environment.

Preferred skill-

• AI/ML certifications from AWS, Azure, or Google Cloud are a plus.

Lead the design of scalable AI and GenAI architectures, including agentic systems, LLM orchestration, and RAG pipelines.

Define and maintain reference architectures for AI applications across cloud platforms (AWS, Azure, GCP).

Architect multi-agent systems with orchestration layers, agent registries, and knowledge graphs.

Translate business goals into AI solution roadmaps, ensuring alignment with enterprise strategy and ROI expectations.

Establish governance frameworks for model lifecycle, data privacy, bias mitigation, and responsible AI practices.

Oversee model selection, fine-tuning, and evaluation, including SLMs, LLMs, and hybrid architectures.

Guide the development of agentic AI applications,support development teams

Lead integration of AI systems with enterprise platforms (SAP, Oracle, CRM, ServiceNow).

Ensure security, scalability, and compliance across deployment environments (cloud-native, hybrid, on-prem).

Mentor AI developers, data scientists, and engineers on agentic design patterns, prompt engineering, and model governance.