Job Summary

Position Title: Data Science Practice

Department/Function: Product Engineering (AI)

Job Scope: Global

Reporting to: CAIO

Location: Noida, India

Work Setting: Onsite

Purpose of the Job: Doceree is building the first proactive intelligence layer for pharma brand teams-an agentic AI system that turns clinical intent signals, campaign data, and market context into a daily brief that drives a decision in under five minutes. We are hiring AI Engineers to build the systems behind that surface.

You will design and ship production AI systems-LLM-powered pipelines, agentic workflows, retrieval over heterogeneous healthcare data, evaluation harnesses, and the orchestration layer that sits between our signal foundation and the brand managers morning brief. You'll work closely with data scientists, product, and platform engineering to take prototypes from a notebook to something a brand team relies on every day.

You'll thrive here if you enjoy living at the seam between LLMs, traditional ML, and well-engineered backend systems and if you care about making AI useful, reliable, and measurably better in a regulated, real-world domain.

Key Responsibilities

- Own the end-to-end technical vision and execution of AI-powered capabilities for specific products, starting with the Daily Command platform, from early prototypes to production systems.
- Design, build, and ship production-grade LLM and agentic AI systems powering anomaly detection, contextual synthesis, next-best-action recommendations, and one-click activation.
- Build and optimize retrieval-augmented generation (RAG) systems leveraging Doceree's clinical intent signals, campaign data, market intelligence, and partner datasets.




- Develop agentic workflows and orchestration patterns including tool use, planning, structured outputs, guardrails, and multi-step reasoning.
- Establish robust evaluation, monitoring, and observability frameworks to measure quality, reliability, business impact, and model performance over time.
- Make pragmatic decisions around model selection, architecture, vendor partnerships, and build-vs-buy tradeoffs across commercial and open-source AI ecosystems.
- Partner closely with Product, Data Science, and Engineering teams to translate ambiguous business opportunities into scalable AI solutions.
- Productionize ML and AI capabilities through APIs, services, and reusable platform components that can be consumed across products.
- Own deployment, reliability, performance, and cost management of AI systems running on AWS.
- Establish best practices for AI engineering, including prompt management, testing, experimentation, security, privacy, and compliance.
- Serve as the technical leader for AI initiatives, mentoring future team members and helping shape the long-term AI roadmap for the organization.
- Stay current with advances in LLMs, agents, retrieval systems, and AI infrastructure, and evaluate where emerging technologies can create competitive advantage.

Qualifications and Experience

- B.Tech / M.Tech / Ph.D. in Computer Science, Artificial Intelligence, Statistics,



or a related quantitative discipline.
- 6-12 years of software engineering and AI/ML experience, including at least 2-3 years building production LLM, generative AI, or agent-based applications.
- Strong hands-on programming experience in Python and up-to-date software engineering practices, including system design, testing, API development, and cloud-native architectures.
- Proven experience building and deploying AI-powered applications using LLMs, RAG, agents, recommendation systems, search, or related technologies.
- Experience with modern AI frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, or equivalent custom implementations.
- Experience designing evaluation frameworks and monitoring production AI systems beyond simple prompt experimentation.
- Strong understanding of classical ML/DL techniques and libraries (scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers, SpaCy, NumPy, Pandas) and when not to use an LLM.
- Experience building and operating services on AWS using technologies such as ECS/EKS, Lambda, SageMaker, Bedrock, or equivalent cloud platforms.
- Ability to operate independently, make architectural decisions, and drive projects from concept through production with minimal oversight.
- Excellent communication skills and the ability to explain technical tradeoffs to both technical and non-technical stakeholders.

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Data Science Practice Professional (Noida)
🏢 Doceree
📍 Noida