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