Lead AI Engineer

Job Description:

AI Lead Engineer

Role Overview

We are seeking aLead Generative AI Engineerwith strong foundations in deep learning transformer architecture and practical experience building GenAI applications beyond basic RAG systems. The ideal candidate has hands-on experience/technical familiarity with LLM fine-tuning multimodal models retrieval systems agentic frameworks retrieval architectures and production-grade ML deployment.

This role will partner with engineering data science and CX teams to build intelligent agents multimodal experiences personalization systems and knowledge-grounded AI solutions that power the future of customer engagement for global brands.

Key Responsibilities

Generative AI Multimodal Systems & Agentic Frameworks

• Build conversational and non-conversational multimodal and agentic AI applications using LLMs and frameworks such as LangChain LangGraph LlamaIndex AutoGen or similar.
• Design AI workflows incorporating reasoning planning tool-use memory grounding and external system integrations.
• Develop Knowledge Graph (KG)-assisted AI systems including entity extraction linking and KG-augmented retrieval.
• Ensure safety consistency and hallucination-control through structured evaluation and guardrails.

Deployment APIs & Cloud Engineering

• Transform models into scalable APIs and microservices using Python FastAPI/Flask Docker.
• Deploy and monitor ML/AI systems in AWS/Azure/GCP optimizing for cost latency and reliability.
• Collaborate with MLOps teams on CI/CD pipelines model versioning monitoring and automated evaluation.
• Work with big data technologies including Apache Spark Hadoop and NoSQL databases such as MongoDB.

Model Development & Applied AI Engineering

• Build and optimize transformer-based and multimodal models using deep learning frameworks (e.g. PyTorch TensorFlow).
• Implement fine-tuning alignment (RLHF/RLAIF) LoRA/QLoRA pruning and model evaluation pipelines.
• Developinformation retrieval systems including hybrid densesparse retrieval ranking knowledge graphs and relevance optimization.
• Build predictive models and ML pipelines from scratch including data preparation feature engineering and model selection.

Collaboration Documentation & Mentorship

• Work cross-functionally with CX engineering and product stakeholders to translate business needs into AI solutions.
• Document models experiments evaluation frameworks and deployment processes.
• Mentor junior engineers and contribute to internal best practices reusable components and R&D initiatives.

Required Technical Skills

• Programming:Python (advanced) SQL; robust experience with API development and data engineering
• Backend Frameworks: Flask FASTAPI Django
• Machine Learning:Predictive modelling deep learning optimization embeddings vector search model evaluation.
• Generative AI:LLMs RAG multimodal architectures agents prompt engineering grounding knowledge graphs.
• Cloud Platforms:AWS Azure or GCP with hands-on experience deploying and scaling AI systems.
• Data Technologies:Apache Spark Hadoop MongoDB; strong understanding of data pipelines and large-scale processing.
• Math Foundations:Linear algebra probability statistics.

Experience Requirements

• Minimum 5-6 yearsof hands-on software development experience including building and deploying machine learning models into production.
• 2 years of experience working with deep learning GenAI or transformer-based architectures.
• Demonstrated experience building GenAI applicationsbeyond simple RAG(e.g. agents multimodal custom LLM fine-tuning).
• Experience integrating AI systems in enterprise-grade environments.

Skill Category

Lead AI Engineer

Transformers & Deep Learning

Applies LoRA/QLoRA distillation debugging optimization.

Generative AI (LLMs & Multimodal)

Builds tool-using pipelines multilingual/multimodal flows.

Information Retrieval & Relevance

Implements hybrid retrieval ranking KG-enhanced semantic retrieval

Predictive Modeling

Builds and tunes end-to-end ML pipelines.

Knowledge Graphs

Builds KG pipelines (entity linking embeddings).

Conversational AI

Multi-turn multilingual dialogue systems with evaluation metrics.

Agentic Frameworks

Multi-step agent workflows with planning & memory.

Model Deployment

Scales services with CI/CD monitoring GPU/accelerator ops.

Cloud & MLOps

End-to-end model lifecycle automation.

Big Data & Pipelines

Uses Spark/Hadoop/MongoDB effectively.

Deep Learning

Understand and applied deep learning architectures RNNs LSTMs Transformers

Attitude & Mindset

• Growth-oriented collaborative and experimentation-driven.
• Strong problem-solving skills with a bias toward action.
• Ability to communicate complex concepts clearly to non-technical stakeholders.
• Open and flexible towards a hybrid work structure with no less than 2-days work from office This is to ensure that the team working in the AI domain regularly connects and does knowledge exchange across projects

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