Senior Data Scientist - ML, Python

Job Summary

Job Description: AI Application Developer / AI Engineer Role Overview

We are seeking an experienced AI Application Developer / AI Engineer to design, build, and deploy end‑to‑end AI solutions using modern machine learning and generative AI techniques. The role involves working hands‑on with data, models, and production systems to deliver scalable, reliable AI applications—without reliance on code‑assist tools such as GitHub Copilot.

--- Key Responsibilities

• Design, develop, and deploy end‑to‑end AI applications from data ingestion to production inference.

• Build data pipelines for data preparation, feature engineering, and model training.

• Select, train, evaluate, and optimize machine learning and deep learning models.

• Develop APIs and services to expose AI models for real‑time and batch use cases.

• Implement monitoring, logging, and model performance tracking in production.

• Collaborate with product, data, and domain teams to translate business requirements into AI solutions.

• Ensure AI solutions meet enterprise standards for security, scalability, and responsible AI usage.

Key Responsibilities

1. Design And Implement Advanced Machine Learning Algorithms Using Tensorflow And Pytorch To Extract Insights From Large Datasets, Delivering Customized Analytical Reports Tailored To The Needs Of Colleagues, Clients, And Stakeholders.

2. Leverage Python And Sql To Develop And Optimize Data Models That Address Specific Organizational Challenges, Ensuring Effective Integration And Management Of Data.

3. Analyze And Mine Large Datasets To Identify Trends And Patterns, Utilizing Advanced Analytics Techniques To Interpret Findings And Provide Actionable Recommendations Based On Experimental Results.

4. Collaborate With Cross-Functional Teams To Identify Opportunities For Utilizing Data Insights To Formulate Strategic Business Solutions That Enhance Operational Efficiency And Product Offerings.

5. Create Comprehensive Visualizations And Reports Using Data Visualization Tools To Communicate Complex Analysis And Results Clearly, Enabling Informed Decision-Making For Customers And Stakeholders.

Skill Requirements

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Other Requirements

Required AI Skill Areas (Core – 4 Skills)1. Machine Learning & Model Development

• Strong understanding of supervised and unsupervised learning techniques.

• Experience with model training, evaluation, and tuning.

• Ability to select appropriate algorithms based on use case and data characteristics.

• Familiarity with evaluation metrics and model validation techniques.

--- 2. Data Engineering & Feature Engineering

• Hands‑on experience with data preprocessing, cleaning, and exploratory data analysis.

• Strong skills in feature engineering and handling real‑world data issues.

• Proficiency in working with structured and semi‑structured data from multiple sources.

• Experience using Python libraries such as Pandas and Num Py, along with SQL.

--- 3. Generative AI / LLM‑Based Application Development

• Experience building applications using Large Language Models (LLMs).

• Strong skills in prompt design, prompt optimization, and template creation.

• Working knowledge of embeddings, vector search, and Retrieval‑Augmented Generation (RAG).

• Experience integrating LLM APIs into enterprise applications.

--- 4. AI System Design, Deployment & MLOps

• Ability to design scalable AI architectures for training and inference.

• Experience deploying models as APIs or services (e.g., using FastAPI or Flask).

• Understanding of model versioning, monitoring, data drift, and retraining strategies.

• Familiarity with containerization and cloud deployment concepts.

--- Technical Skills

• Strong proficiency in Python

• Experience with ML/DL frameworks such as Py Torch or Tensor Flow

• Familiarity with REST APIs, microservices, and cloud platforms (GCP)

• Knowledge of model lifecycle management tools (e.g., MLflow – preferred)

--- Domain & Soft Skills

• Ability to work independently without code‑generation tools

• Strong analytical and problem‑solving skills

• Clear communication with technical and non‑technical stakeholders

• Healthcare Domain knowledge is a strong plus

--- Nice to Have

• Experience with responsible AI practices and explainability

• Exposure to AI ethics, bias mitigation, and governance

• Experience working in regulated or enterprise environments