Lead AI Engineer

Job Description:

AI Lead Engineer

Role Overview

We are seeking a Lead Generative AI Engineer with 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.
• Develop information retrieval systems, including hybrid dense-sparse 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 years of 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 applications beyond 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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