Principal Data Scientist/Machine Learning Engineer - NLP/Deep Learning

About the Role :

We are seeking a Principal Data Scientist / ML Engineer to lead the development, experimentation, and productionization of our core AI/ML models. This is a technical leadership role where you will define the machine learning strategy, choose appropriate algorithms (e.g., advanced NLP, deep learning, graph networks), and ensure our models deliver transformative, measurable value to our enterprise clients. You will drive the entire ML lifecycle from research and development to deployment and MLOps.

Key Technical Responsibilities :

1. AI/ML Strategy and Model Development

- Research & Innovation: Drive the adoption of state-of-the-art AI/ML techniques, with a focus on Generative AI and Large Language Models (LLMs), to solve complex, unstructured data problems for B2B applications.

- Model Ownership: Design, implement, and rigorously validate highly accurate and scalable predictive, classification, and optimization models in a production environment.

- Algorithm Selection: Evaluate and recommend optimal algorithms, frameworks (e.g., PyTorch, TensorFlow), and compute resources based on data constraints and latency requirements.

2. MLOps and Productionization

Deployment Pipeline: Partner with the MLOps and Backend teams to build robust, automated pipelines for model training, testing, versioning, and continuous deployment (CI/CD) using tools like Kubeflow or MLflow.

Real-time Inference: Optimize model serving architecture to achieve ultra-low latency inference, integrating seamlessly with our microservices and enterprise client systems.

Monitoring & Governance: Define model monitoring metrics (drift, bias, feature importance) and establish governance frameworks to ensure fairness, compliance, and sustained performance in production.

3. Data Engineering Partnership

Data Requirements: Define the data acquisition, cleansing, and labeling strategies necessary for high-quality model training, working closely with Data Engineers to structure complex, large-scale enterprise datasets.

A/B Testing: Design and execute controlled A/B experiments to measure the incremental business impact and ROI of new model iterations before full deployment.

What You'll Bring (Mandatory Skills & Experience)

Educational Excellence: B.Tech/M.Tech/Ph.D. in Computer Science, Statistics, or a quantitative field from an IIT, NIT, BITS Pilani, or IIIT is mandatory.

Experience: 4-7 years of dedicated, hands-on experience in building and deploying ML models to solve real-world, large-scale problems in a product company setting.

Core Skills: Expert proficiency in Python, SQL, and ML frameworks (PyTorch/TensorFlow).

ML Domain: Deep knowledge of MLOps principles, distributed training, and model explainability (XAI).

Cloud & Tools: Experience working with cloud-based ML services (e.g., AWS SageMaker, GCP AI Platform) and containerization (Docker/Kubernetes).

Logistics and Compensation

Location: This is a Hybrid Work / Work from Office Only position in Bangalore. No remote options are available.

Compensation: Highly competitive compensation package in the range of ?50 LPA - ?85 LPA + significant ESOPS, determined by years of experience and domain expertise.