Job Summary

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.

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 NumPy, 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 PyTorch or TensorFlow
• 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

Key Responsibilities

Skill Requirements

Other Requirements