aimlroles

Lead AI Engineer

Dentsuaegis Open · verified Oct 22, 2026
Bengaluru Full-time Senior AI Engineering

At a glance

Senior AI Engineering role at Dentsuaegis. Bengaluru · full-time.

Pay not stated AI Engineering salary

RoleAI Engineering
SenioritySenior
LocationBengaluru
EmploymentFull-time
PostedOct 8, 2026 · 2w ago
Role brief

aimlroles summary, based on the employer's posting.

What you'll do

  • Build conversational, multimodal and agent-based AI applications, then design workflows for planning, memory and tool use
  • Create knowledge-graph features for entity extraction, linking and retrieval, with evaluation and safeguards to limit hallucinations
  • Package models as APIs and microservices, deploying and monitoring them across AWS, Azure or GCP
  • Develop transformer and multimodal models, fine-tune them, and build retrieval and predictive-model pipelines
  • Work with CX, engineering and product teams to shape AI solutions, document your work and mentor junior engineers

What you bring

  • At least five years building software, including taking machine-learning models into production
  • Two or more years working with deep learning, generative AI or transformer architectures
  • You have built generative AI applications beyond basic retrieval, such as agents, multimodal systems or custom model tuning
  • You bring advanced Python skills, SQL, API development and experience integrating AI into enterprise environments

Who this fits

This senior, hands-on engineering role suits someone who can build and deploy production AI systems while mentoring junior engineers. It is a permanent, full-time position based in Bengaluru.

From the employer

Job Description:

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.

Must Have :

  • Minimum 5+ 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.

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.

Location:

DGS India - Bengaluru - Bhartiya City Block 1 (inactive)

Brand:

Merkle

Time Type:

Full time

Contract Type:

Permanent