Data Scientist

We are looking for a skilled and hands-on

Data Scientist

with 3–8 years of experience in developing and deploying machine learning models—ranging 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.

Exp-3-8 Years Mode-Remote Np-Immediate-15 Days

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 4–5 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.