Staff Machine Learning Engineer
At a glance
Senior Machine Learning Engineering role at Weekday AI. Bengaluru · full-time.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Set the technical direction for machine learning and generative AI work, reviewing system designs and engineering practices
- Build and launch predictive, recommendation, forecasting, language-processing and deep-learning systems for production use
- Guide solutions through data exploration, feature design, model testing, deployment, monitoring and ongoing improvement
- Create reusable ML components and oversee platform pipelines, model serving, tracking, retraining and governance
- Work with product, engineering and business partners to turn needs into AI projects, define success measures and assess results
What you bring
- At least ten years working across machine learning, data science or AI engineering
- A track record of taking AI or ML products into large-scale production, plus leadership of complex technical work
- Strong Python, SQL and distributed-computing skills, with practical knowledge of machine-learning frameworks
- Hands-on experience with generative AI solutions, including language models, retrieval-augmented generation or agentic systems
Who this fits
This Bengaluru-based role suits a senior technical leader who can shape architecture, mentor ML engineers and data scientists, and work across product, engineering and business teams. The position is full-time and on-site in Bengaluru.
From the employer
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