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