Data Scientist

Mott MacDonald

Data Scientist leading the integration of Generative AI and Agentic Workflows into global projects. Collaborating with engineers and consultants to enhance infrastructure sustainability and efficiency.

Posted 4/30/2026full-timeNoida • 🇮🇳 IndiaMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AzureDockerKerasKubernetesPythonPyTorch

About the role

Key responsibilities & impact

Lead the end-to-end development and deployment of AI/ML projects, ensuring they solve real-world engineering and consultancy challenges
Design and implement Agentic AI architectures (using LangChain, Lang Graph, etc.) to automate complex reasoning tasks across technical documentation and project management
Rapidly develop Proof of Concepts (PoCs) and transition them into high-availability, enterprise-grade applications hosted on Azure
Work alongside structural engineers, environmental consultants, and digital architects to integrate AI/ML into the wider ecosystem
Contribute to the scale-up of AI solutions, ensuring cost-optimized deployment (LlmOps) and alignment with our commitment to ethical & responsible AI

Requirements

What you’ll need

5+ years of experience in working with NLP problems
2+ years of experience in working with LLM
Proven experience fine-tuning and deploying LLM models (GPT, Llama 3, Falcon)
Hands-on experience with agentic AI orchestration design patterns with building systems using LangChain, Crew AI, or Llama Index
Mastery of RAG (Retrieval Augmented Generation), RAG Fusion, and Chain-of-Thought prompting to extract insights from massive technical datasets
Deep understanding of Transformers (BERT, GPT), NLU, and NLG principles
5+ years of experience with algorithms including XGBoost, Random Forest, and advanced regression models/time series models
Proficiency in Python with a focus on clean code, design patterns, and scalable architecture
Practical experience with PyTorch or Keras for computationally intensive modelling
Strong track record of deploying models within Azure ML and managing the full ML lifecycle
Experience with Vector Databases (Pinecone, Milvus, or PgVector) and Graph Databases to support complex data relationships
Knowledge of LlmOps, Docker, and Kubernetes to ensure model reliability and cost-efficiency
Comfortable taking additional challenges and excellent communication ability

Benefits

Comp & perks

Flexible working
Critical illness and compassionate leave
Paternity Leave
Group term life insurance, and Group medical insurance coverage
Career mobility options
Short and Long-term global employment opportunities
Global collaboration and knowledge sharing

ATS Keywords

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Hard Skills & Tools

NLPLLMfine-tuning LLM modelsagentic AI orchestrationRAGTransformersalgorithmsPythonPyTorchKeras

Soft Skills

communicationproblem-solvingcollaborationadaptabilityleadership