Data Science Practice

Doceree Inc.; Dadri; On-site; INR 2,600,000 - 5,200,000; Full time

Benefits offered by this job

Competitive Salary

Health care benefits

Generous Leave Policy

Performance-based bonuses

Job summary

Doceree Inc. is hiring AI Engineers to build production AI systems, including LLM-powered pipelines and agentic workflows, to support pharma brand teams. You will collaborate with data scientists, product, and platform engineering to move prototypes into daily use across products.

You will thrive at the intersection of LLMs, traditional ML, and engineered back-end systems, focusing on reliable, compliant AI in a regulated healthcare domain. Knowledge of AWS and modern AI tooling is essential.

Qualifications

• B. Tech / M.Tech / Ph.D. in Computer Science, AI, 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 modern 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 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.

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.

Skills

Python

Cloud architecture

LLMs / AI systems

Technical leadership

Communication

Education

B. Tech / M.Tech / Ph.D. in Computer Science / AI / Statistics

Tools

LangChain

LangGraph

LlamaIndex

Semantic Kernel

PyTorch

TensorFlow

SageMaker

ECS/EKS

Job description

Doceree is the world’s only AI-powered Operating System for healthcare marketing. We aim to be a catalyst for change, improving healthcare by enabling more meaningful interactions with HCPs. Our patented AI brings deeper context to every HCP touchpoint—understanding who HCPs are, what they’re exploring, how they engage, and when they make decisions, all in a privacy-compliant way. With this enriched context, every message becomes clearer, more relevant, and more likely to drive better healthcare outcomes.

In our journey of 5 years, we have earned four patents for innovation and even won a Silver award at the Cannes Lions, one of the world’s top advertising honors. Over the years, we’ve also had the opportunity to work with 115+ pharma companies and collaborate with 30+ media publications, 150+ EHR platforms, 2,000+ publisher networks, and 35+ hospitals and health systems. Our ecosystem connects us with more than 6 million verified doctors worldwide. Today, Doceree operates across 25+ countries, with teams based in New Jersey, London, and India.

Our Core Belief

Technology can connect the fragmented healthcare ecosystem to deliver information when it is most needed to improve patients' outcomes.

We are expanding our footprints across the globe and enhancing our services, offering, and developing new products and solutions to address the unmet needs of industry. Doceree is operating in 22 countries currently with offices in US, UK and India.

Job Scope: Global
Reporting to: CAIO

Location: Noida, India

Work Setting: WFO

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 manager's 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 Requirement: Experience, Skills & Education

• 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 modern 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.

Doceree India Benefits

• Competitive Salary Package
• Generous Leave Policy
• Performance-Based Bonuses
• Health Care Benefits