AI Engineer

Dentsu International Americas, LLC; Pune District; Ibrido; INR 1.500.000 - 2.600.000; Tempo pieno

Descrizione del lavoro

Merkle in Pune is seeking an AI Engineer to build Generative AI, multimodal, and agentic solutions that power customer engagement for global brands. You will develop scalable APIs and ML pipelines, work with LangChain/LangGraph/LlamaIndex, and deploy in cloud environments while collaborating with CX and engineering teams.

The role emphasizes hands-on GenAI, transformer models, and knowledge-grounded AI with an emphasis on production readiness and robust evaluation.

Competenze

• 3-4 years of hands-on software development with production ML deployments.
• 2+ years of deep learning, GenAI, or transformer-based architectures.
• Experience building GenAI applications beyond simple RAG (agents, multimodal, fine-tuning).
• Experience integrating AI systems in enterprise-grade environments.

Mansioni

• Build GenAI, multimodal, and agentic AI apps using LLMs and frameworks (LangChain, LangGraph, LlamaIndex, AutoGen).
• Design AI workflows with reasoning, planning, tool-use, memory, grounding, and external integrations.
• Develop KG-assisted AI systems with entity extraction and retrieval.
• Deploy models as scalable APIs/microservices; monitor in cloud environments; collaborate with MLOps.

Conoscenze

Python

LLMs

GenAI

Multimodal

Cloud platforms

Data pipelines

Transformer models

API development

Docker

Spark/Hadoop

MongoDB

LangChain/LangGraph

LlamaIndex/AutoGen

Strumenti

Docker

LangChain

LangGraph

LlamaIndex

AutoGen

Descrizione del lavoro

Job Description:

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.

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 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.

Skill 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

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.

Model Deployment

Deploys models as basic APIs.

Cloud & 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

Location:

DGS India - Pune - Indiqube Orchid

Brand:

Merkle

Time Type:

Full time

Contract Type:

Permanent