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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.

Designs and develops integrated circuits. Oversees definition, design, verification, and documentation for ASIC development. Determines architecture design, logic design, and system simulation. Defines module interfaces/formats for simulation. Contributes to the development of multidimensional designs involving the layout of complex integrated circuits. Evaluates all aspects of the process flow from high-level design to synthesis, place and route, and timing and power use. Analyzes equipment to establish operation data, conducts experimental tests, and evaluates results. May also review vendor capability to support development.
- Knowledge: Having wide-ranging experience, uses professional concepts and company objectives to resolve complex issues in creative and effective ways.
Strong project management skills
Leads design and delivery of new
products/process
Functional breadth and depth, plus expert in complementary fields
Applies broad concepts and theories to achieve innovative and effective solutions to complex problems - Job Complexity / Contribution : Works on complex issues where analysis of situations or data requires an in-depth evaluation of variable factors. Exercises judgment in selecting methods, techniques and evaluation criteria for obtaining results. Networks with key contacts outside own area of expertise.
Will champion significant projects, programs and business initiatives using demonstrated creativity and ingenuity
Team leader
Leads major projects
Influences or impacts others'
priorities, decisions or activities
Escalation point for complex issues
Coaches and mentors other junior team members - Supervision : Incumbents provide a leadership role for the work group through knowledge in his/her area of specialization. Generally free to determine work priorities based on general direction from managers.
Determines methods and procedures on new assignments
Consults with management on long-range goals
Determines own priorities, both tactical and strategic

As a Senior Machine Learning Scientist, you will work closely with engineers and product to design, develop, evaluate and deploy Gen AI powered solutions for scalable, customer-facing applications. Your work will focus on applying state-of-the-art agentic capabilities and driving business impact based on rigorous evaluations and experimentation.



Key Job Responsibilities and Duties:

Design end-to-end agentic systems, delivering high-quality, performant, and efficient solutions to production

Build agentic solutions for different tasks and use cases using state-of-the-art techniques

Develop and carry out evaluation strategies, including formulating new metrics and building evaluation judges

Pioneer and promote best practices and adoption of new technology in GenAI application development

Lead and mentor other team members, providing technical guidance and timely feedback to develop the team and motivate them to achieve their goals.

Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into ML solutions.

Conduct deep data analysis to evaluate model performance, label quality, features exploration.

Work closely with ML engineers to ensure and improve the solutions latency/throughput meets product requirements and ensure deployment of your model to production.

abra R&D is looking for a QA Engineer! Key Responsibilities:
* Execute manual, regression, integration, and end-to-end testing.
* Validate bug fixes and support release validation and rollout testing.
* Investigate customer-reported issues and reproduce defects for troubleshooting and resolution.
* Perform hardware and sensor validation activities.
* Create, maintain, and update test cases and test documentation.
* Support test automation initiatives and maintain existing automated test suites.
* Assist with Jenkins job validation and vulnerability testing activities.
* Collaborate closely with Development, DevOps, and Support teams to ensure product quality.
* Identify, document, track, and verify defects throughout the testing lifecycle.