AI/ML Intern

The AI Acceleration (AIA) team is a central product and engineering function responsible for designing, building, and scaling AI‑powered capabilities across Pfizer’s Commercial, Marketing, and Field organisations. AIA focuses on turning advanced analytics and Generative AI into practical, scalable, and secure products that measurably improve how teams plan, decide, and execute in the real world.

AIA operates using a product‑centric pod model, bringing together data scientists, AI engineers, product owners, UX, and platform engineers into small, outcome‑driven teams aligned to specific products and use cases. Each pod owns end‑to‑end delivery—from discovery and design through production, operations, and continuous improvement—ensuring clear accountability and faster execution.

About the Role

We are looking for an AI/ML Intern to support the development of AI-powered applications with a focus on LLM-based workflows, observability, evaluation, and reliable API integrations. This is a time-bound internship (not a full-time role) designed to provide hands-on experience building production-grade AI systems under the guidance of senior engineers and data scientists.

Internship details:

• Duration: 3–6 months
• Commitment: Full-time (40 hrs/week)
• Location: Mumbai

This role is dynamic, fast-paced, highly collaborative, and covers a broad range of strategic topics that are critical to our business. The successful candidate will join GCA colleagues worldwide, who are constantly supporting business transformation through their proactive thought leadership, innovative analytical capabilities, and their ability to communicate highly complex and dynamic information in new and creative ways.

Responsibilities

• Build and maintain small Python modules that support AI/ML and LLM workflows (data prep, prompt utilities, wrappers, evaluators).
• Assist with instrumenting services for observability and tracing (e.g., Langfuse/OpenTelemetry) to enable end-to-end debugging and monitoring.
• Support LLM evaluation tasks such as dataset creation, test case design, and basic quality metrics (accuracy, completeness, groundedness).
• Implement and test REST API integrations (JSON/HTTP) with guidance—handling retries, timeouts, error codes, and logging.
• Participate in debugging and performance analysis using traces, logs, and metadata; document findings and proposed fixes.
• Work in agile sprints with a mentor; provide regular progress updates and ask for feedback early and often.
• Write clear documentation (setup, runbooks, README) and contribute to code reviews as a learner.

Requirements

Qualifications & Experience

• Currently pursuing a B.E./B.Tech/M.Tech in Computer Science, IT, ECE, Data Science, or a related field.
• Academic projects, internships, or personal projects demonstrating practical Python/ML/AI skills are preferred.

Relevant Experience

• Strong Python fundamentals (syntax, functions, modules, virtual environments).
• Good grasp of data structures and basic object-oriented programming.
• Comfort with Git basics (branching, commits, pull requests) and collaborative coding.
• Familiarity with REST APIs, JSON, and HTTP concepts; ability to test APIs using tools or simple scripts.
• Understanding of ML basics (training vs. inference, evaluation metrics) through coursework or projects.
• Intro-level awareness of NLP/LLM concepts (prompts, tokens, latency/cost trade-offs) is a plus.

Good to Have

• Exposure to LLM tools or frameworks (LangChain, LangGraph, OpenAI APIs, etc.).
• Familiarity with observability or tracing tools (Langfuse, OpenTelemetry, logging frameworks).
• Basic SQL knowledge and understanding of data retrieval concepts.
• Experience using Postman or curl for API testing.
• Docker or cloud fundamentals (AWS/Azure/GCP).

Professional Characteristics

• Adaptability and Flexibility: Interns should be able to adjust and thrive in changing environments, embracing new tasks and applying knowledge in diverse contexts.
• Strong Communication: Effective communication skills are essential for workplace interactions, including conveying ideas clearly and listening actively.
• Positivity: A positive attitude shows a willingness to work hard and learn, contributing to a harmonious work environment.
• Self-Starter: Takes an active role in one’s own professional development; stays abreast of analytical trends, and cutting-edge applications of data.