Staff Machine Learning Engineer

Weekday (YC W21); Bengaluru; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Weekday (YC W21) in Bengaluru is seeking a Staff Machine Learning Engineer to lead the architecture, development, and scaling of enterprise‑grade ML and Generative AI platforms. 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 help shape our AI roadmap and build intelligent products for thousands of businesses

Qualifications

• Must have 8+ 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.

Responsibilities

• Define and drive the technical vision for ML and GenAI initiatives.
• Lead architecture reviews and establish best practices for scalable AI systems.
• Mentor ML engineers and data scientists across teams.
• Partner with Engineering leadership to build scalable, reliable, and secure AI platforms.
• Design, develop, and deploy large-scale ML solutions in production environments.
• Drive the ML lifecycle from data acquisition to deployment and monitoring.
• Establish model governance, explainability, and compliance standards.

Skills

Applied Machine Learning

Architectural thinking

Mentoring

Stakeholder management

Cross-functional leadership

Tools

Docker

Kubernetes

AWS

Azure

GCP

Vector Databases

APIs & Microservices

Job description

• Feature engineering
• Multi-agent workflows

This role is for one of our clients

Industry: Software Development

Seniority level: Mid-Senior level

Min Experience: 10+ years

Location: Bengaluru

JobType: full-time

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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.

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

• 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