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AI Engineer

Dentsuaegis

AI Engineer Role Overview We are seeking a 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.

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.
  • LLMs, RAG, multimodal architectures, agents, prompt engineering, grounding, knowledge graphs.

Requirements

  • 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.
  • AWS, Azure, or GCP with hands-on experience deploying and scaling AI systems.
  • Apache Spark, Hadoop, MongoDB; strong understanding of data pipelines and large-scale processing.
  • Linear algebra, probability, statistics.
  • Requirements Minimum 3-4 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.
  • Information Retrieval & Relevance Uses vector DBs for retrieval, knowledge graphs - – Demonstrated by project experience
  • Predictive Modeling Builds and deploys ML models; applies evaluation metrics – Demonstrated by project experience
  • Knowledge Graphs Integrates and retrieves from existing KGs Conversational AI Text-only chatbots; intent models.
  • Agentic Frameworks Basic agent/tool calling.
  • & MLOps Uses cloud AI services.
  • Big Data & Pipelines Writes SQL/Python ETL.
  • 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

Preferred

  • Category AI Engineer Transformers & Deep Learning Understands transformers; fine-tunes small models.
  • Generative AI (LLMs & Multimodal) Works with LLM APIs; simple RAG and KAG – Demonstrated by project experience

Model Deployment Deploys models as basic APIs.

Location

DGS India - Pune - Indiqube Orchid Brand: Merkle Time Type: Full time

Contract Type

Permanent

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