# Staff Machine Learning Engineer

> Weekday AI · Bengaluru, India · Full-time · Posted 2026-09-22

**Workplace:** on_site

**Department:** Weekday's Client via platform

## Description

**This role is for one of Weekday’s clients**

  
Min Experience: 10+ years  
Location: Bengaluru  
JobType: full-time

We are seeking a highly experienced **Staff Machine Learning Engineer** to lead the architecture, development, and scaling of enterprise-grade Machine Learning and Generative AI platforms.

As a senior technical leader, you will drive AI strategy, establish engineering best practices, mentor ML engineers, and collaborate cross-functionally with Product, Engineering, Data, and Business stakeholders to deliver measurable business outcomes. You will play a critical role in shaping our AI roadmap and building intelligent products that impact thousands of businesses globally.

The ideal candidate will have **8+ years of experience** building and deploying large-scale ML systems, deep expertise across the full ML lifecycle, and hands-on experience delivering production-grade Generative AI solutions at scale.

## Requirements

Key Responsibilities

### Technical Leadership

-   Define and drive the technical vision for Machine Learning and Generative AI initiatives.
-   Lead architecture reviews and establish best practices for scalable AI systems.
-   Mentor and guide ML engineers and data scientists across teams.
-   Influence product strategy through AI-driven innovation and technical thought leadership.
-   Partner with Engineering leadership to build scalable, reliable, and secure AI platforms.

### Machine Learning & Data Science

-   Design, develop, and deploy large-scale ML solutions in production environments.
-   Build advanced predictive models, recommendation systems, forecasting solutions, NLP applications, and deep learning systems.
-   Drive the complete machine learning lifecycle:

-   Problem definition
-   Data acquisition and exploration
-   Feature engineering
-   Model development
-   Model evaluation and validation
-   Production deployment
-   Monitoring, governance, and continuous improvement

-   Develop frameworks and reusable components to accelerate ML development across teams.
-   Establish model governance, explainability, fairness, and compliance standards.

### Generative AI & LLM Applications

Architect and deliver enterprise-scale GenAI solutions leveraging:

-   OpenAI
-   Azure OpenAI
-   Anthropic Claude
-   Llama
-   Mistral
-   Gemini

Design and implement:

-   Advanced RAG architectures
-   Agentic AI systems
-   Multi-agent workflows
-   AI orchestration frameworks
-   Prompt engineering and evaluation frameworks
-   Fine-tuning and model adaptation pipelines
-   Knowledge graph-assisted AI systems
-   AI observability and evaluation frameworks

Lead experimentation and adoption of emerging AI technologies to create competitive advantage.

### Platform Engineering & MLOps

Architect scalable ML platforms and infrastructure.

Build and optimize end-to-end ML pipelines.

Drive MLOps best practices including:

-   CI/CD for ML
-   Model serving
-   Feature stores
-   Experiment tracking
-   Monitoring and observability
-   Automated retraining pipelines
-   Model governance and security

Optimize system performance, scalability, reliability, and cost efficiency.

### Cross-Functional Collaboration

-   Partner with Product Managers, Engineering leaders, and Business stakeholders to identify high-impact AI opportunities.
-   Translate business problems into scalable AI solutions.
-   Define success metrics and measure business impact.
-   Drive AI adoption and technical excellence across the organization.

Preferred Qualifications

### Experience

-   10+ years of experience in Machine Learning, Data Science, and AI Engineering.
-   Proven track record of delivering production-grade AI/ML products at scale.
-   Experience leading complex technical initiatives and influencing engineering direction.
-   Experience mentoring engineers and driving technical excellence across teams.

### Technical Skills

Strong expertise in Python, SQL, and distributed computing frameworks such as Spark.

Deep knowledge of machine learning and deep learning frameworks:

-   PyTorch
-   TensorFlow
-   Scikit-learn

Strong expertise in:

-   Large Language Models (LLMs)
-   Retrieval-Augmented Generation (RAG)
-   Agentic AI Systems
-   Reinforcement Learning concepts
-   AI Evaluation Frameworks

Hands-on experience with:

-   Docker
-   Kubernetes
-   AWS, Azure, or GCP
-   Vector Databases
-   API and Microservices Architecture

Expertise in:

-   MLOps
-   Model Deployment
-   Feature Stores
-   Experiment Tracking
-   Observability and Monitoring

### Leadership Attributes

-   Strong architectural and systems-thinking mindset.
-   Ability to influence without authority and drive cross-functional alignment.
-   Exceptional communication and stakeholder management skills.
-   Passion for mentoring, innovation, and continuous learning.

Must-have skills

Applied Machine Learning

Good-to-have skills

Machine Learning, AI ENGINEERING

## Apply

[Apply at Weekday AI](https://apply.workable.com/weekday-1/j/D25053C563/apply)

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