AI/ML Innovation Engineer

You will contribute to one or more of the following high-impact areas:

• AI Copilots & Agents: Build LLM-powered assistants, implement prompt engineering, Retrieval-Augmented Generation (RAG) pipelines, and agentic workflows and related applications
• Intelligent Document & Contract AI: Extract, classify, and query insights from unstructured documents (contracts, policies, SOPs); build NLP pipelines for semantic search and summarization
• Data & AI Platforms:  Work on data ingestion, transformation, and feature engineering pipelines; contribute to scalable AI/ML infrastructure
• Real-time AI Systems: Build APIs, dashboards, and backend services that power AI-driven insights for end users
• Analytics & Decision Engines:  Develop anomaly detection models (e.g., claims, pricing, Gross-to-Net); build forecasting and optimization models to support commercial decisions
• Collaborate with cross-functional technical teams to translate business needs into technical specifications, focusing on AI-driven automation and insights
• Participate in code reviews, design discussions, and adopt best practices across MLOps and software engineering

Functional Skills:

Must-Have Skills:

• Strong programming skills in Python, PySpark, and SQL
• Solid understanding of Data Structures & Algorithms, OOP, and System Design fundamentals
• Hands-on experience with core Machine Learning techniques (Regression, Classification, Clustering)
• Working knowledge of NLP fundamentals (tokenization, embeddings, transformers)
• Exposure to LLMs (OpenAI, HuggingFace, Anthropic, etc.) and prompt engineering / GenAI workflows
• Experience with data manipulation libraries (Pandas, NumPy) and working with both structured and unstructured data
• Familiarity with REST APIs and backend development basics
• Mandatory hands-on experience with 2–3 real AI/ML projects
• AI/GenAI: LLM-based chatbot with RAG (document Q&A), AI summarization tools, or agent-based multi-step reasoning systems
• Engineering: Deployed ML models (API or web app), data pipelines (ETL/ELT)

Good-to-Have Skills:

• Experience with RAG pipelines and vector databases (FAISS, Pinecone, Chroma, Weaviate)
• Hands-on experience with LangChain, LlamaIndex, or agent frameworks (AutoGen, CrewAI)
• Exposure to MLOps tools (Docker, CI/CD, MLflow, Kubeflow, Airflow)
• Knowledge of time-series forecasting and anomaly detection techniques
• Frontend basics (Streamlit, React) for building dashboards and demos
• Familiarity with cloud platforms (AWS, Azure, or GCP)
• Experience with Databricks for data analytics and ML workflows
  • Foundational understanding of the US pharmaceutical ecosystem, relevant datasets (e.g., claims, prescription data), and Patient Support Services offerings