Manager - Automation

GMR Group; Hyderabad; On-site; INR 1,800,000 - 2,600,000; Full time

Job summary

GMR Group is seeking a senior ML/AI engineer to build, deploy, and scale machine learning and GenAI solutions across its airports, energy, and infrastructure verticals.

You will own end-to-end ML lifecycle, from data ingestion and feature engineering to model training, evaluation, deployment, and drift management, leveraging AWS SageMaker, Azure ML Studio, Google AI Platform and CI/CD practices.

Qualifications

• Hands-on experience in building and deploying ML/AI solutions at scale.
• Proficient in Python, SQL and ML frameworks (scikit-learn, TensorFlow, XGBoost, PyTorch).
• Experience with cloud-native ML tools: AWS SageMaker, Azure ML Studio, Google AI Platform.

Responsibilities

• Build, deploy, and scale ML/AI solutions across GMR’s verticals (airports, energy, infrastructure).
• Own full ML lifecycle from data ingestion to model deployment and monitoring.
• Collaborate with GenAI teams to develop intelligent workflows and automation.

Skills

Python

SQL

ML frameworks

NLP

Computer Vision

DevOps

Education

Masters preferable

Tools

AWS SageMaker

Azure ML Studio

Google AI Platform

Docker

Git

Step Functions

Lambda

OpenCV

Hugging Face

Job description

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Company: GMR Group

JOB PURPOSE

Role would be responsible to build, deploy, and scale machine learning and AI solutions across GMR’s verticals. This role will build and manage advanced analytics initiatives, predictive engines, and GenAI applications — with a focus on business outcomes, model performance, and intelligent automation. Reporting to the Head of Automation & AI, you will operate in a high-velocity, product-oriented environment with direct visibility of impact across airports, energy, infrastructure and enterprise functions

ORGANISATION CHART

Head- Automation & AI --> Manager-AI & ML --> Reports To --> Position Title

KEY ACCOUNTABILITIES

Responsibilities

Key Performance Indicators (KPIs)

AI & ML Development

• Build and deploy models using supervised, unsupervised, and reinforcement learning techniques for use cases such as forecasting, predictive scenarios, dynamic pricing & recommendation engines, and anomaly detection, with exposure to broad enterprise functions and business
• Lead development of models, NLP classifiers, and GenAI-enhanced prediction engines.
• Design and integrate LLM-based features such as prompt pipelines, fine-tuned models, and inference architecture using Gemini, Azure OpenAI, LLama etc.

End-to-End Solutioning

• Translate business problems into robust data science pipelines with emphasis on accuracy, explainability, and scalability.
• Own the full ML lifecycle from data ingestion and feature engineering to model training, evaluation, deployment, retraining, and drift management.
• Deploy production-grade models using AWS, GCP, or Azure AI platforms and orchestrate workflows using tools like Step Functions, SageMaker, Lambda, and API Gateway.
• Build and optimise ETL/ELT pipelines, ensuring smooth integration with BI tools (Power BI, QlikSense or similar) and business systems.
• Data compression and familiarity with cloud finops will be an advantage, have used some tools like kafka, apache airflow or similar
• 100% compliance to processes

EXTERNAL INTERACTIONS

• Consulting and Management Services provider
• IT Service Providers / Analyst Firms

INTERNAL INTERACTIONS

• GCFO and Finance Council, Procurement council, IT council, HR Council (GHROC)
• GCMO/ BCMO

FINANCIAL DIMENSIONS

OTHER DIMENSIONS

EDUCATION QUALIFICATIONS

• Masters preferable

RELEVANT EXPERIENCE

• 5 - 8years of hands-on experience in machine learning, AI engineering, or data science, including deploying models at scale.
• Strong programming and modelling skills in some like Python, SQL, and ML frameworks like scikit-learn, TensorFlow, XGBoost, PyTorch.
• Demonstrated ability to build models using supervised, unsupervised, and reinforcement learning techniques to solve complex business problems.
• Technical & Platform Skills
• Proven experience with cloud-native ML tools: AWS SageMaker, Azure ML Studio, Google AI
• Platform.
• Familiarity with DevOps and orchestration tools: Docker, Git, Step Functions, Lambda,Google AI or similar
• Comfort working with BI/reporting layers, testing, and model performance dashboards.
• Mathematics and Statistics
• Linear algebra, Bayesian method, information theory, statistical inference, clustering, regression etc
• Collaborate with Generative AI and RPA teams to develop intelligent workflows
• Participate in rapid prototyping, technical reviews, and internal capability building
• NLP and Computer Vision
• Knowledge of Hugging Face Transformers, Spacy or similar NLP tools
• YoLO, Open CV or similar for Computer vision.

COMPETENCIES

• Personal Effectiveness
• Problem Solving & Analytical Thinking
• Planning & Decision Making
• Capability Building