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As a Machine Learning Engineer, you will work closely with experienced engineers and ML scientists to build scalable, production-grade GenAI applications. Your work will focus on designing, training, and deploying ML systems leveraging LLMs,, recommendation systems, and agent-based architectures, using state-of-the-art technologies. These solutions will directly power customer-facing experiences and play a key role in shaping the future of AI-driven travel products.

Key Job Responsibilities and Duties:

Deploying machine learning models: Design, develop and deploy in collaboration with scientists, scalable machine learning models and algorithms that provide content related insights and generative AI applications, ensuring scalability, efficiency, and accuracy.

Evaluating possible architecture solutions by taking into account cost, business requirements, emerging technologies, and technology requirements, like latency, throughput, and scale.

Generative AI Development: Contribute to the development of generative models such as GPT (Generative Pre-trained Transformer) variants or similar architectures for creative content generation, Q&A, chatbots, translation or other innovative applications.

Deployment and integration: Work closely with software engineers to integrate machine learning models into production systems. Ensure seamless deployment and efficient model inference in real-time environments. Collaborate with DevOps to implement effective monitoring and maintenance strategies.

Owning a service end to end by actively monitoring application health and performance, setting and monitoring relevant metrics and acting accordingly when violated.

Maintain clean, scalable code, ensuring reproducibility and easy integration of models into production environments, including CI/CD.

Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.

We are looking for a talented and hands-on DevOps Engineer to join our team and play a key role in building, maintaining, and scaling our cloud infrastructure and delivery pipelines.
What youll do:
Design, implement, and maintain cloud infrastructure across AWS and Azure
Build and manage CI/CD pipelines using Azure DevOps
Develop and maintain Infrastructure as Code using Terraform and Terragrunt
Manage containerized environments using Docker
Administer and maintain Windows and Linux servers
Implement and manage monitoring, logging, and alerting systems using Grafana and related tools
Automate operational processes using PowerShell, Bash, and Python
Collaborate closely with development teams to improve performance, reliability, and system availability
Troubleshoot and resolve issues in complex production environments.

As a Machine Learning Engineer, Youll work with top notch engineers and machine learning scientists on bringing it to the next level and enabling optimal customer experience while having a significant impact on the business. The work will focus on data and ML foundations for reusable training, optimization and deploying causal machine learning models.



Key Job Responsibilities and Duties:

Model evaluation and optimization: Conduct detailed model evaluation metrics and validation to ensure accuracy, reliability, and scalability. Optimize model performance by fine-tuning hyper parameters, feature engineering, and applying techniques such as ensemble learning and continuous learning.

Data pre-processing and analysis: Collaborate with data scientists and data engineers to collect, clean, pre-process, and transform large and wide datasets for model features and data monitoring. Conduct exploratory data analysis (EDA) to uncover insights and identify patterns that boost the model performance.

Building machine learning models: Design, develop and deploy in collaboration with scientists, scalable machine learning models and algorithms that provide personalized recommendations to users.

Deployment and integration: Work closely with software engineers to integrate machine learning models into production systems. Ensure seamless deployment and efficient model inference in real-time environments. Collaborate with DevOps to implement effective monitoring and maintenance strategies.

Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.

We are looking for a highly technical, hands-on Tech Lead to build and lead a Core Engineering Team that operates across all R&D squads.
This team will act as a force multiplier - solving complex system challenges, driving architectural improvements, and enabling development teams to move faster with higher quality.
This is a deeply hands-on role (50%), combining system design, architecture, and execution in a data-critical, high-scale environment.
What Makes This Role Different:
Not a feature team - focus on system-level problems
Not a traditional manager - strong hands-on ownership
Not advisory - drives and executes real changes in production
Works across teams - true cross-team impact
Responsibilities:
Cross-Team Technical Leadership:
Lead a Core Team working across multiple squads
Identify system-wide bottlenecks, risks, and architectural gaps
Drive alignment on engineering standards and best practices
Partner closely with Tech Leads, Product, and Dev teams
Architecture & System Design:
Own and evolve system architecture (Monolith → Modular / Services)
Reduce tight coupling and improve system boundaries
Lead major refactoring and modernization efforts
Ensure scalability, performance, and resilience
Microservice architecture (from modular monolith to microservices)
Hands-On Development (70%):
Write complex production code across services
Lead by example in code quality and design
Take ownership of the most challenging technical problems
Engineering Excellence & Enablement:
Define and enforce development standards (Code Reviews, Design Patterns)
Improve observability (monitoring, logging, alerting)
Build reusable tools, infrastructure, and shared components
Enable teams to deliver faster with higher quality
Advantage for AI driven standard validations
Mentorship & Technical Growth:
Mentor engineers and Tech Leads across teams
Raise the engineering bar across the organization
Support teams in production debugging and solving complex production issues
Tech Stack:
Backend: Node.js, (Express,nestJS), PHP (advantage)
Frontend: React, Redux, React Hooks
Data: MySQL, SQL, Redis
Infrastructure: AWS
Advantage: Experience with agent frameworks (agentic flows)
Success in This Role:
Teams deliver faster with fewer blockers
System architecture becomes more scalable, modular and stable
Production stability and observability improve
Engineering standards are consistent and adopted
The Core Team becomes a trusted partner across R&D.

We are looking for a highly technical and hands-on AI Transformation Lead to drive the implementation and adoption of AI across the organization.
This is a builder role, not a strategy-only position. You will be responsible for identifying high-impact opportunities, designing and implementing AI-powered solutions, and integrating them into our existing systems and workflows across R&D, Support, Sales, and Operations.
The ideal candidate combines a strong engineering background with the ability to translate business needs into scalable, production-ready solutions - and ensure they are actually adopted and deliver measurable impact.
Role Mission:
Lead the companys AI transformation end-to-end through practical implementation - building, integrating, and scaling AI-driven workflows and internal tools that improve efficiency, quality, and business performance across all departments.
What Youll Do:
Build & Implement AI Solutions (Core Focus):
Design, build, and deploy AI-powered workflows, automations, and internal tools
Develop rapid POCs and evolve them into production-ready solutions
Work hands-on with LLMs, APIs, and automation frameworks
Ensure solutions are scalable, maintainable, and aligned with system architecture
Integrate with Systems & Architecture:
Integrate AI solutions into existing platforms (CRM, support systems, internal tools, data flows)
Design robust integrations using APIs and orchestration layers
Ensure alignment with engineering standards, security, and data integrity
Drive Cross-Company Impact:
Identify high-value use cases across R&D, Support, Sales, Domain, and Operations
Translate business workflows into efficient, AI-driven processes
Partner with stakeholders to ensure solutions solve real problems
Own Adoption & Impact:
Ensure solutions are used, not just built
Define and track KPIs (efficiency, time saved, quality improvements, adoption rates)
Continuously iterate based on feedback and real-world usage
Enable & Standardize:
Establish best practices for AI usage across the company
Guide teams on how to effectively use AI tools in their daily work
Act as the internal expert for AI workflows, tools, and capabilities
What Success Looks Like (First 3-6 Months):
Delivered multiple production-level AI solutions with measurable business impact
Reduced manual work and improved efficiency across at least 2-3 departments
Successfully integrated AI workflows into core systems and daily operations
Achieved strong adoption across teams (beyond experimentation).

As a Machine Learning Scientist, you will design, build, and deploy advanced models that guide pricing and promotional optimization across us. You will work closely with other scientists, engineers, analysts, and product teams to translate complex business challenges into scalable, data-driven solutions that deliver measurable impact.

Key Job Responsibilities and Duties:

Develop and deploy models for causal inference, uplift estimation, and optimization to measure and maximize the incremental effect of price and promotion decisions.

Design and improve dynamic pricing algorithms that balance competitiveness, conversion, and profitability.

Contribute to the development of platform capabilities, enhancing experimentation, simulation, and decision-support capabilities.

Partner with product and business stakeholders to translate scientific insights into actionable strategies.

Stay up to date with the latest advances in machine learning, causal modeling, and pricing optimization, and apply them pragmatically at scale.