Data Scientist AI Engineer

Pune - India

Monthly Salary: Not Disclosed

Experience Required: 4-8years

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

Who are we

Fulcrum Digital is an agile and next-generation digital accelerating company providing digital transformation and technology services right from ideation to implementation. These services have applicability across a variety of industries including banking & financial services insurance retail higher education food healthcare and manufacturing.

About the Role

We are looking for a skilled and hands-on Data Scientist with 45 years of experience in developing and deploying machine learning modelsranging from traditional ML algorithms to advanced deep learning and Generative AI systems. The ideal candidate brings a strong foundation in classification anomaly detection and time-series modeling along with hands-on experience in deploying and optimizing Transformer-based models. Familiarity with quantization fine-tuning and RAG (Retrieval-Augmented Generation) is highly desirable.

Responsibilities

Design train and evaluate ML models for tasks such as classification anomaly detection forecasting and natural language understanding.
Build and fine-tune deep learning models including RNNs GRUs LSTMs and Transformer architectures (e.g. BERT T5 GPT).
Develop and deploy Generative AI solutions including RAG pipelines for use cases such as document search Q&A and summarization.
Perform model optimization techniques such as quantization for improving latency and reducing memory/compute overhead in production.
Optionally fine-tune LLMs using Supervised Fine-Tuning (SFT) and Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA or QLoRA.
Define and track relevant evaluation metrics; continuously monitor model drift and retrain models as needed.
Collaborate with cross-functional teams (data engineering backend DevOps) to productionize models using CI/CD pipelines.
Write clean reproducible code and maintain proper versioning and documentation of experiments.

Requirements

Required Skills

45 years of hands-on experience in machine learning or data science roles.
Proficient in Python and ML/DL libraries: scikit-learn pandas PyTorch TensorFlow.
Strong knowledge of traditional ML and deep learning especially for sequence and NLP tasks.
Experience with Transformer models and open-source LLMs (e.g. Hugging Face Transformers).
Familiarity with Generative AI tools and RAG frameworks (e.g. LangChain LlamaIndex).
Experience in model quantization (e.g. dynamic/static quantization INT8) and deployment on constrained environments.
Knowledge of vector stores (e.g. FAISS Pinecone Azure AI Search) embeddings and retrieval techniques.
Proficiency in evaluating models using statistical and business metrics.
Experience with model deployment monitoring and performance tuning in production environments.
Familiarity with Docker MLflow and CI/CD practices.

Preferred Qualifications

Experience fine-tuning LLMs (SFT LoRA QLoRA) on domain-specific datasets.
Exposure to MLOps platforms (e.g. SageMaker Vertex AI Kubeflow).
Familiarity with distributed data processing (e.g. Spark) and orchestration tools (e.g. Airflow).
Contributions to research papers blog posts or open-source projects in ML/NLP/GenAI.

Required Skills:

Required Skills 45 years of hands-on experience in machine learning or data science roles. Proficient in Python and ML/DL libraries: scikit-learn pandas PyTorch TensorFlow. Strong knowledge of traditional ML and deep learning especially for sequence and NLP tasks. Experience with Transformer models and open-source LLMs (e.g. Hugging Face Transformers). Familiarity with Generative AI tools and RAG frameworks (e.g. LangChain LlamaIndex). Experience in model quantization (e.g. dynamic/static quantization INT8) and deployment on constrained environments. Knowledge of vector stores (e.g. FAISS Pinecone Azure AI Search) embeddings and retrieval techniques. Proficiency in evaluating models using statistical and business metrics. Experience with model deployment monitoring and performance tuning in production environments. Familiarity with Docker MLflow and CI/CD practices. Preferred Qualifications Experience fine-tuning LLMs (SFT LoRA QLoRA) on domain-specific datasets. Exposure to MLOps platforms (e.g. SageMaker Vertex AI Kubeflow). Familiarity with distributed data processing (e.g. Spark) and orchestration tools (e.g. Airflow). Contributions to research papers blog posts or open-source projects in ML/NLP/GenAI.

Who are weFulcrum Digital is an agile and next-generation digital accelerating company providing digital transformation and technology services right from ideation to implementation. These services have applicability across a variety of industries including banking & financial services insurance ret...