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About Quadratyx We are a product-centric insight & automation services company globally. We help the worlds organizations make better & faster decisions using the power of insight & intelligent automation. We build and operationalize their next-gen strategy, through Big Data, Artificial Intelligence, Machine Learning, Unstructured Data Processing and Advanced Analytics. Quadratyx can boast of more extensive experience in data sciences & analytics than most other companies in India. We firmly believe in Excellence Everywhere. Senior AI ML/ Gen AI Engineer Job / Role Information Designation Senior Data Engineer Big Data & GenAI Function Technical Location: Hyderabad, Chennai Job Description Purpose of the Job/ Role: We are looking for a highly skilled and hands-on Data Engineer with strong expertise in Big Data technologies, distributed data processing, and backend engineering. The ideal candidate should have practical experience in building scalable batch and real-time data pipelines using frameworks such as PySpark, Kafka, and streaming platforms. The candidate should also possess strong Python programming skills and experience building APIs/microservices using modern backend frameworks. This role is best suited for engineers who enjoy solving large-scale data engineering problems and working closely with AI/GenAI-driven applications. We are specifically looking for candidates with strong hands-on coding and distributed systems experience, and not candidates whose experience is predominantly limited to traditional ETL tools, low-code cloud pipelines, or Snowflake-centric workflows. Key Requisites Design, develop, and optimize scalable batch and streaming data pipelines Build large-scale distributed data processing systems using PySpark Develop real-time streaming solutions using Kafka and/or Apache Flink Build and maintain APIs and backend services using Python-based microservice frameworks Work with structured and semi-structured datasets across multiple storage systems Optimize pipeline performance, scalability, and fault tolerance Collaborate with AI/ML and GenAI teams to support LLM and RAG-based applications Participate in architecture discussions, debugging, code reviews, and production deployments Ensure reliability, monitoring, and maintainability of data engineering systems Mandatory Skills Strong hands-on experience with PySpark and distributed data processing (must-have) Strong programming expertise in Python Hands-on experience building APIs and backend services using: Flask FastAPI or similar microservice frameworks Good understanding of Big Data ecosystem and streaming architectures Experience With Apache Kafka Apache Flink (preferred) PostgreSQL Experience building end-to-end batch and real-time data engineering pipelines Good understanding of scalable system design, partitioning, optimization, and distributed processing concepts Understanding of microservices architecture and API integrations Experience working in Linux-based environments Strong analytical and problem-solving skills GenAI / AI Engineering Expectations Candidates should also have practical exposure to modern GenAI concepts and tools, including: Strong analytical and problem-solving skills Working knowledge of LLMs and prompt engineering Understanding of RAG (Retrieval-Augmented Generation) concepts Experience Using GenAI Frameworks/tools Such As LangChain LlamaIndex (good to have) Exposure to vector databases, embeddings, or AI-assisted workflows is a plus Good to Have / Bonus Skills Experience with QuestDB Exposure to real-time analytics and streaming systems Understanding of distributed systems internals Experience with Docker/containerization and deployment workflows Familiarity with monitoring and logging frameworks Knowledge of cloud platforms is an added advantage Important Note This role is focused on core data engineering, distributed systems, and backend engineering. Candidates With Predominantly Snowflake-only experience Low-code ETL pipeline development Pure cloud ETL orchestration backgroundsmay not be the right fit for this requirement unless they also possess strong hands-on Big Data engineering and programming experience. Working Relationships Reporting to Vice President External Stakeholders Clients Preferred Candidate Profile Strong ownership mindset and problem-solving ability Minimum 5-8 years of work experience as a Data Manager in an IT organization (preferably Analytics / Big Data/ Data Science / AI background). Comfortable working independently on complex engineering problems Ability to work in fast-paced product/research-oriented environments Passion for learning modern AI and data technologies Good communication and collaboration skills Quadratyx is an equal opportunity employer - we will never differentiate candidates on the basis of religion, caste, gender, language, disabilities or ethnic group. Quadratyx reserves the right to place/move .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.