Home/Jobs/AI Engineer AI Engineer Prescient Healthcare Group Gurugram 3-4 years Today $26.5K–33.7K/yr Full-time Hybrid Skills Required LLM RAG Agentic AI Prompt Engineering structured-output parsing Python async FastAPI REST API SQL relational databases evaluation frameworks evaluation sets LangGraph Strands Description AI Engineer role focused on building and deploying GenAI and agentic AI systems for competitive intelligence in the pharmaceutical and life sciences space. The work includes retrieval pipelines, evaluation frameworks, APIs, and dashboards in a fast-paced product environment.

Company: Prescient Role: AI Engineer Location: Flexible hybrid working arrangement at Gurugram office Experience 3–4 years experience writing production Python, including async, typed and tested code

Experience shipping an agentic system involving tool calling, multi-turn state and failure handling

Experience building or maintaining an evaluation set for LLM output

Production prompt engineering and structured-output parsing expertise

Experience building one RAG stack end to end

REST API development using FastAPI or similar frameworks

Relational database fundamentals and ability to write non-trivial SQL queries Qualification Tech

M.Tech

MCA or equivalent education Responsibilities Design and implement agentic AI systems that query external data sources and process pharmaceutical and biotech information

Develop robust retrieval pipelines over specialized life sciences content to extract valuable competitive intelligence

Create and maintain evaluation harnesses to ensure accuracy, reliability, and ethical operation of AI systems

Engineer production-grade prompts and structured-output parsing mechanisms for consistent AI performance





Build and optimize RAG stacks to enhance knowledge retrieval from domain-specific content

Develop REST APIs using FastAPI or similar frameworks to integrate AI capabilities into the product ecosystem

Design and implement dashboards that effectively communicate AI-generated insights to analysts and clients

Collaborate with data scientists and domain experts to continuously improve AI system performance and relevance

Write clean, typed, and well-tested Python code following best practices for maintainability and scalability

Troubleshoot complex issues in multi-turn agent interactions and implement robust error handling mechanisms Additional Responsibilities Work in a fast-paced environment with tight feedback loops, real users, and short release cycles

Contribute directly to the technology-driven competitive advantage in the life sciences consulting sector Nice To Have Agent harness engineering, including execution environments, sandboxing, permissions, context management, retries, interrupts and sub-agent spawning

Agent control-loop design, including self-verification, stopping criteria, turn limits and recoverable error handling

Experience with LangGraph, Strands, MCP or AWS Bedrock AgentCore

Familiarity with Braintrust, LangSmith, Promptfoo, DeepEval or equivalent evaluation tooling





Life sciences or pharma data exposure

Front-end development for internal tools using HTML, JavaScript, charting libraries or React

Experience with Docker, ECS or Kubernetes More Skills GenAI, LLM agents, retrieval pipelines, async Python, typed Python, tested code, dashboards, tool calling, multi-turn state, failure handling, pharmaceutical data, biotech information, competitive intelligence Other Opportunity to work on cutting-edge AI systems with direct impact on pharmaceutical and life sciences decision-making

Cooperative environment with a team of technology and domain experts

Clear career progression path with mentorship from senior technical leaders

Professional development opportunities including training and certifications

Competitive compensation package with comprehensive benefits Follow for Daily AI Tutorials Prepare for this role Recommended resources to build the skills for this position. Sponsored. Generative AI with Large Language Models Coursera Comprehensive LLM course covering transformer architecture, fine-tuning, RLHF, and deployment.

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