AI Engineer

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