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
