Technical Lead AI

Weekday AI (YC W21); Mumbai; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Weekday AI (Client Role) in Mumbai seeks a senior leader to guide AI/ML operations and platform engineering across the organization, ensuring technical excellence and widespread adoption of AI solutions.

The role emphasizes architecture, mentoring AI/Data/Platform teams, deploying cloud-native AI/ML stacks with Kubernetes and serverless, and governing production AI/ML systems with strong observability and security.

Qualifications

• 10+ years IT/software engineering/data science experience.
• 5+ years direct AI/ML tech production experience.
• 3+ years in technical leadership or senior engineering roles.

Responsibilities

• Lead deep-dive investigations across AI/ML stacks and infra.
• Mentor AI Engineering, Data Engineering, and Platform teams.
• Drive AI platform enablement and best practices.
• Provide architecture guidance for AI/ML solutions.
• Oversee selection/integration of AI tools and cloud services.
• Establish monitoring/logging/observability for AI/ML systems.

Skills

ML Frameworks

ML Platforms

LLM GenAI

Data Processing

Cloud Platforms

Kubernetes

MLOps Tools

ETL Tools

Observability Tools

Programming Languages

Git

Tools

Azure AI

AWS SageMaker

Google Vertex AI

Databricks

Job 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