AI Engineer – Generative AI, LLM & MLOps (Karnataka)

Key Responsibilities

– Design, develop, and deploy machine learning, statistical, deep learning, and

Generative AI models for enterprise use cases.

– Perform data exploration, feature engineering, model training, evaluation, and

optimization on structured and unstructured datasets.

– Build predictive, prescriptive, and descriptive analytics solutions aligned with

business objectives.

– Develop and fine-tune AI/ML models including classical ML, Deep Learning, NLP,

Time-Series Forecasting, and Large Language Models (LLMs).

– Design and implement Retrieval-Augmented Generation (RAG) pipelines using

enterprise knowledge sources and Vector Databases.

– Build reusable Prompt Templates and Prompt Engineering strategies for

enterprise AI applications.

– Apply Anthropic Constitutional AI principles to develop protected, reliable, and

responsible AI applications.

– Collaborate with Data Engineering and Platform teams to productionize models

using MLOps and LLMOps best practices.

– Develop AI experimentation, evaluation, and benchmarking frameworks using

MLflow and enterprise AI evaluation methodologies. Key Responsibilities

– Design, develop, and deploy machine learning, statistical, deep learning, and

Generative AI models for enterprise use cases.

– Perform data exploration, feature engineering, model training, evaluation, and

optimization on structured and unstructured datasets.

– Build predictive, prescriptive, and descriptive analytics solutions aligned with

business objectives.

– Develop and fine-tune AI/ML models including classical ML, Deep Learning, NLP,

Time-Series Forecasting, and Large Language Models (LLMs).

– Design and implement Retrieval-Augmented Generation (RAG) pipelines using

enterprise knowledge sources and Vector Databases.

– Build reusable Prompt Templates and Prompt Engineering strategies for

enterprise AI applications.

– Apply Anthropic Constitutional AI principles to develop protected, reliable, and

responsible AI applications.

– Collaborate with Data Engineering and Platform teams to productionize models

using MLOps and LLMOps best practices.

– Develop AI experimentation, evaluation, and benchmarking frameworks using

MLflow and enterprise AI evaluation methodologies.