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Job Details : Role : Senior DS (ATA) Experience : 7 - 10 Years Mode of work : Hybrid Model Work Location : Hyderabad Role Overview : Analyse client data landscapes of varying quality and consistency, and design AI/ML solutions that fit within our existing product and solution offering.Build and maintain end-to-end GenAI and Agentic AI solutions RAG pipelines, LLM integrations, NLP workflows using LangChain, HuggingFace and FastAPI.Design, implement and maintain data infrastructure and pipelines supporting machine learning operations (ETL, PySpark, Databricks).Collect, clean and pre-process large multi-source datasets to ensure accuracy and consistency.Implement and tune machine learning and deep learning models using TensorFlow, PyTorch, scikit-learn and Keras.Evaluate LLM and RAG solution quality using frameworks such as RAGAS; conduct ad-hoc analysis to answer specific business questions.Develop and integrate APIs to expose AI/ML capabilities to downstream applications.Build and maintain dashboards and monitoring tools (Power BI or similar) to track key performance indicators.Deploy and operate solutions on Azure and AWS using Docker, App Insights and standard DevOps practices.Identify and evaluate new technologies to improve performance, maintainability and reliability of existing ML systems.Collaborate with business stakeholders, data scientists, software engineers, QA and DevOps teams; communicate insights and recommendations clearly. Roles & Responsibilities : Overall 5 years of experience in AI/ML implementation, data engineering and software development.Hands-on end-to-end delivery experience across GenAI, Agentic AI, NLP, RAG, LangChain, HuggingFace, RAGAS, FastAPI and OCR.Strong proficiency in Python with proven API integration and software engineering discipline.Experience working with cloud platforms Microsoft Azure and AWS including Databricks, Docker and App Insights.Working knowledge of ML/DL frameworks : TensorFlow, PyTorch, scikit-learn, Keras, Pandas.Experience with data technologies data wrangling, ETL/data integration, data profiling, data quality and data discovery; PySpark exposure.Familiarity with metadata management tools and visualisation platforms such as Power BI.Clear understanding of data engineering, classical ML, AI models and LLMs.Strong analytical and problem-solving skills, with all-round communication and collaborative working style.Bachelor's/Master's in Computer Science or Information Science (B. Tech, MCA, MS Computers). .
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.