About the Chance
A dynamic tech firm operating at the intersection of enterprise AI and advanced data science, we build scalable machine learning systems that drive automation, predictive analytics, and intelligent decision-making for mid-to-large businesses across finance, logistics, and SaaS verticals. Our Data Science + AI Engineering team turns complex data into production-grade AI solutions leveraging cloud-native architectures, MLOps pipelines, and generative AI frameworks to deliver measurable ROI.

Role Responsibilities
Design, train, and deploy ML models (supervised/unsupervised) for business-critical use cases including forecasting, classification, and anomaly detection.
Build and maintain MLOps pipelines using Azure ML / SageMaker / Vertex AI for model versioning, retraining, and CI/CD integration.
Collaborate with product and engineering teams to operationalize AI models into scalable microservices using Python, FastAPI, or Flask.
Apply NLP and GenAI techniques (LangChain, RAG, LLM fine-tuning) to enhance chatbots, document processing, and search systems.
Optimize model performance through feature engineering,



hyperparameter tuning, and A/B testing in production environments.
Ensure model governance monitoring drift, bias, explainability (SHAP/LIME), and compliance with enterprise audit standards.

Skills Qualifications

Must-Have
Python
Scikit-learn
Pandas
TensorFlow / PyTorch
SQL
Azure ML / SageMaker / Vertex AI
Git
LangChain

Preferred
LLM fine-tuning (LoRA, PEFT)
FastAPI / Flask
MLflow / Weights Biases

Perks Culture Highlights
On-site work with flexible core hours in Ahmedabad cooperative, innovation-driven culture.
Access to cutting-edge AI tooling and cloud credits for experimentation and upskilling.
Opportunity to own end-to-end AI product modules and present impact to C-suite stakeholders.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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