We are looking for an experienced AI Lead / Data and Artificial Intelligence Enablement Lead to identify high-value artificial intelligence opportunities, recommend suitable solution approaches, and enable data and engineering teams to adopt artificial intelligence capabilities.

The role involves working across generative artificial intelligence, retrieval-augmented generation, artificial intelligence agents, traditional machine learning, predictive modelling, and intelligent automation. The candidate will be responsible for converting business opportunities into secure, scalable, measurable, and production-ready solutions.

Major Responsibilities

- Partner with business, data, and technology teams to identify and prioritise artificial intelligence use cases.

- Evaluate whether rules, analytics, traditional machine learning, retrieval-augmented generation, generative artificial intelligence, or automation is the most suitable solution.

- Define artificial intelligence architectures, technology selections, delivery approaches, and implementation roadmaps.

- Lead the design and delivery of retrieval-augmented generation solutions, including document processing, chunking, embeddings, vector search, retrieval, re-ranking, and grounded response generation.

- Guide predictive modelling initiatives involving classification, regression, forecasting, anomaly detection, recommendations, and optimization.

- Assess managed models, open-source models, fine-tuning approaches, and custom modelling solutions.

- Develop secure integrations between artificial intelligence services, enterprise data platforms, applications, and business workflows.

- Establish evaluation frameworks to measure model quality, retrieval relevance, hallucination, accuracy, latency, cost, and business value.

Requirements

- Significant experience delivering artificial intelligence, machine learning,



or advanced analytics solutions in production environments.

- Strong understanding of the complete artificial intelligence lifecycle, from problem definition and data preparation to evaluation, deployment, and monitoring.

- Practical experience implementing retrieval-augmented generation solutions.

- Strong knowledge of large language models, embeddings, vector databases, retrieval, re-ranking, prompt engineering, and model evaluation.

- Strong programming skills in Python and SQL.

- Experience with common data science and machine learning libraries.

- Knowledge of contemporary cloud data platforms such as Snowflake and Databricks.

- Experience developing structured, semi-structured, and unstructured data pipelines.

- Experience integrating artificial intelligence solutions through application programming interfaces, applications, and enterprise workflows.

- Knowledge of machine learning operations, large language model operations, continuous integration and continuous delivery, experiment tracking, model registries, and production monitoring.

Benefits

- Professional growth and career development in artificial intelligence and machine learning.

- Opportunities to work on generative artificial intelligence, predictive modeling, and intelligent automation solutions.

- Exposure to artificial intelligence architecture, experimentation, and production implementation.

- Collaboration with business, data engineering, and technology teams.

- Opportunity to develop reusable artificial intelligence architectures, frameworks, and engineering patterns.

- Experience in responsible artificial intelligence, governance, security, and regulatory compliance.

- Opportunities to coach teams and build organizational artificial intelligence capabilities.

- Exposure to modern cloud data platforms and enterprise artificial intelligence technologies.

📌 AI Lead / Data & AI Enablement Lead (Hyderabad)
🏢 Multiplier Ai
📍 Hyderabad