AI Technical Lead

Weekday AI · Mumbai, India · Full-time · Posted 2026-08-13

Workplace: on_site

Department: Weekday's Client via platform

Description

This role is for one of Weekday’s clients

Min Experience: 12+ years
Location: Mumbai, Maharashtra, India
JobType: full-time

The incumbent shall be responsible for leading AI/ML operational and platform engineering functions across the organization. The focus of this role is to provide and ensure technical excellence in the AI/ML technology landscape and drive the adoption of AI solutions across business units.

Requirements

Relevant Experience:

10 years’ experience in IT, software engineering, or data science related positions
5 years of direct experience with AI/ML technologies, platforms, and solutions in production environments.
3 years in technical leadership, architecture, or senior engineering role.
Experience in Designing and implementing large-scale AI/ML solutions and systems.
Experience working with cloud platforms and understanding of cloud-native architectures.
Experience with DevOps, CI/CD pipelines, and containerized deployments.

Responsibilities:

Deep dive technical investigation, analysis and troubleshooting across AI/ML technology stacks, frameworks, and infrastructure using appropriate diagnostic and monitoring tools.
Provide technical leadership and mentoring to AI Engineering, Data Engineering, and Platform teams, fostering a culture of technical excellence and continuous improvement.
Drive the enablement of AI/ML platforms, tooling, and best practices across the organization.
Provide architecture guidance and technical oversight for AI/ML solution design, implementation, and optimization.
Lead the evaluation, selection, and integration of AI/ML tools, frameworks, and cloud services (e.g., Azure AI, AWS SageMaker, Google Vertex AI).
Establish and maintain monitoring, logging, and observability standards for AI/ML systems and models.
Investigate opportunities for optimization of AI/ML technology stacks, including model performance tuning and infrastructure efficiency.
Work with solution architects and business stakeholders to translate business requirements into technical AI/ML solutions.
Provide support and enablement for containerized and cloud-native environments, specifically Kubernetes and serverless platforms.
Ensure compliance, security, and governance best practices are implemented across all AI/ML solutions.
Stay current with emerging AI/ML technologies, frameworks, and industry best practices.

Mandatory Skills:

Machine Learning Frameworks (TensorFlow, PyTorch, scikit-learn, XGBoost)
AI/ML Platforms (Azure AI, AWS SageMaker, Google Vertex AI, Databricks)
Large Language Models and GenAI (transformers, RAG, prompt engineering, LLMOps)
Data Processing and Analytics (Spark, Hadoop, pandas, SQL)
Cloud Platforms (Azure, AWS, GCP)
Container Orchestration (Kubernetes, Docker)
MLOps and Model Deployment tools (MLflow, Kubeflow, DVC, Weights & Biases)
Data Engineering and ETL tools
Monitoring, Logging, and Observability tools (Prometheus, ELK, Grafana, DataDog)
Scripting and Programming Languages (Python, Java, Scala, SQL)
Git and Version Control Systems

Must-have skills

AI/ML technology, AI/ML operational

Good-to-have skills

Engineering Manager

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