Data Science / AI Developer

Role Overview

We are seeking hands-on Data Science / AI Developers to design, build, productionize, and operate machine learning and AI solutions that support U.S. HCP Field Engagement initiatives. In this role, you will partner closely with product, engineering, and data platform teams to deliver reliable, scalable models and AI-driven features that align with our client's data governance standards and measurable business outcomes.

Key Responsibilities

• Deliver end-to-end ML/AI solutions, including problem definition, data preparation, feature engineering, model training, evaluation, deployment, and ongoing monitoring.

• Build and deploy NLP and LLM-based solutions such as Retrieval-Augmented Generation (RAG) pipelines, prompt engineering workflows, fine-tuning strategies, recommendation systems, and predictive analytics models.

• Develop scalable APIs and services for model serving using frameworks such as FastAPI or Flask, and integrate them with data and event pipelines.

• Implement MLOps best practices, including experiment tracking, CI/CD pipelines, model versioning and registries, drift monitoring, and reproducibility.

• Collaborate with cross-functional teams including product, data engineering, compliance, and operations to ensure solutions meet business, regulatory, and governance requirements.

• Write high-quality, maintainable code along with unit tests, documentation, and design artifacts; actively participate in architecture and design reviews.

• Apply secure-by-design and privacy-by-design principles in compliance with client's data and AI governance standards.

Core Qualifications

• 3–5+ years of experience in applied machine learning, AI, or data science software development (or equivalent project-based experience).

• Strong proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, and Hugging Face.

• Solid SQL skills and hands-on experience with data wrangling and feature engineering.

• Proven experience building and deploying production-grade ML services and APIs.

• Practical MLOps experience, including CI/CD pipelines (e.g., GitHub Actions), model registries, and experiment tracking.

• Hands-on experience with modern LLM workflows including prompt design, fine-tuning, embeddings, evaluation, and vector databases.

• Strong understanding of statistical modeling, predictive analytics, and model optimization techniques.

• Experience designing, evaluating, and tuning ML models for both predictive and generative use cases.

• Familiarity with experiment design, hyperparameter tuning, and robust model evaluation.

• Understanding of responsible AI practices such as bias detection, explainability, and regulatory compliance.

• Comfort with model evaluation metrics including precision, recall, F1, ROC-AUC, BLEU, perplexity, and error analysis.

Preferred Skills

• Experience with enterprise data and AI platforms such as Databricks or Amazon SageMaker.

• Familiarity with cloud environments (AWS), containerization, and orchestration tools like Docker and Kubernetes.

• Experience with ML observability, including model and data drift monitoring, lineage, and auditability.

• Strong software engineering fundamentals including design patterns, testing strategies, and code reviews.

Education

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field (or equivalent practical experience).

Must-Have Skill Ratings

• AI / ML: 4/5

• API Development: 4/5

• MLOps (CI/CD, GitHub): 3/5