Staff Machine Learning Engineer (L4)

Accountabilities

Build innovative AI/ML capabilities: Design, develop, and deploy new machine learning functionality that supports intelligent engagement, contextual understanding, and personalized customer experiences across digital communication channels.
Lead end-to-end technical initiatives: Take ownership of technically ambitious projects, translating early-stage concepts and ambiguous requirements into robust solutions, from initial research and experimentation through production deployment.
Design and execute experiments: Develop rigorous experiments to evaluate new ideas, test hypotheses, measure performance, and validate potential improvements, delivering actionable results within short development cycles.
Advance LLM research and implementation: Investigate and apply state-of-the-art techniques in large language models, LLM orchestration, retrieval-augmented generation, and contextual intelligence to solve complex machine learning challenges.
Develop scalable machine learning systems: Build and optimize production-grade ML services capable of supporting high-volume data processing, streaming workloads, real-time inference, and demanding enterprise requirements.
Improve personalization and contextual intelligence: Develop capabilities that help applications and AI agents maintain relevant customer context, understand historical interactions, and deliver consistent, personalized experiences.
Leverage modern AI development tools: Proactively evaluate and integrate emerging AI frameworks, development stacks, and automation tools to accelerate implementation and focus engineering effort on high-value product differentiation.
Apply strong ML fundamentals: Use statistical machine learning, transformer architectures, predictive modeling, and other relevant techniques to develop effective solutions to complex data and personalization problems.
Establish engineering best practices: Promote high standards for system architecture, code quality, experimentation, reliability, and maintainability through technical design reviews and engineering guidance.
Mentor and support engineers: Share expertise, provide constructive feedback, and help teammates strengthen their technical capabilities without relying on formal managerial authority.
Collaborate across functions: Work closely with product, engineering, and other stakeholders to align technical solutions with business objectives, communicate findings, and ensure successful delivery.
Continuously explore new technologies: Stay informed about advances in AI, machine learning, and cloud engineering, rapidly acquiring new skills and applying relevant innovations to evolving product requirements.
Contribute to distributed team execution: Coordinate effectively with colleagues across locations and time zones, maintaining clear communication and strong collaboration in a remote-first environment.
Requirements
Extensive machine learning experience: At least 8 years of applied machine learning or artificial intelligence experience, with a demonstrated track record of independently owning technically ambitious systems from design through implementation and production.
End-to-end engineering ownership: Proven ability to take complex projects from concept to delivery, make sound technical decisions, and drive execution without requiring fully defined requirements or established solutions.
Comfort with ambiguity: A strong bias for action, a builder mindset, and the ability to work effectively in early-stage or rapidly evolving product environments where experimentation and rapid iteration are essential.
Python proficiency: Strong programming skills in Python, with experience developing maintainable, efficient, and production-ready machine learning applications.
Cloud engineering expertise: Hands-on experience building and deploying cloud-based services using AWS, Google Cloud Platform (GCP), or Microsoft Azure.
Scalable data and inference systems: Experience working with high-volume data, streaming pipelines, real-time inference workloads, and diverse data storage technologies.
Machine learning fundamentals: Strong understanding of statistical machine learning algorithms, transformer models, large language models, and their practical applications in production environments.
Research and experimentation: Ability to evaluate emerging ML techniques, formulate testable hypotheses, design meaningful experiments, and translate research findings into reliable software solutions.
Technical leadership: Demonstrated ability to raise engineering standards through architectural guidance, design reviews, mentorship, and the development of shared technical practices.
Communication and collaboration: Excellent written and verbal communication skills, with the ability to explain complex technical concepts, align stakeholders, and collaborate effectively across multidisciplinary teams.
Autonomy and adaptability: Ability to learn new technologies quickly, prioritize effectively, and deliver high-quality results in a fast-paced environment.
AI agents and conversational systems (preferred): Experience building autonomous agents capable of handling multi-turn conversations while maintaining long-term context, consistency, and relevant customer information.
Predictive modeling and user behavior (preferred): Strong background in modeling user behavior, designing experiments, and applying causal inference or counterfactual methods when full randomization is not possible.
Distributed team experience (preferred): Previous experience collaborating with globally distributed engineering teams across different locations and time zones.
Location and availability: Must be based in India, with eligibility to work in the applicable jurisdiction. Occasional travel may be required for team gatherings, project meetings, or customer engagements.
Benefits
Remote-first working model: Work remotely from eligible locations in India, including Karnataka, Tamil Nadu, Telangana, Maharashtra, and New Delhi.
Competitive compensation: Receive a competitive pay package aligned with your skills, experience, and location.
Generous paid time off: Access time-off benefits designed to support personal commitments and work-life balance.
Parental leave: Benefit from parental leave provisions to support employees and their families.
Wellness leave: Access wellness leave to help maintain your physical and mental wellbeing.
Healthcare benefits: Receive healthcare coverage and other benefits according to the applicable local employment package.
Retirement savings: Access a retirement savings program, subject to local eligibility and plan terms.
Technical ownership: Lead ambitious AI/ML projects and influence the design of intelligent customer engagement technologies.
Continuous learning: Explore emerging AI frameworks, LLM technologies, machine learning techniques, and modern cloud infrastructure.
Global collaboration: Work with talented professionals in a distributed, international environment that values diverse perspectives and shared learning.
Career development: Strengthen your technical leadership, research, and production engineering expertise while contributing to high-impact products.
Community engagement: Participate in supported volunteering and donation initiatives that encourage employees to create positive change in their communities.
Collaborative culture: Join a team that values innovation, initiative, inclusion, experimentation, and continuous improvement.