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Position: GenAI, Data engineer, ML engineering : Generative AI / Agentic AI Engineer / Data Engineer / ML Engineer Job Profile Specification: Generative AI / Agentic AI Engineer / Data Engineer / ML Engineer (56 years) Role summary: Senior-level engineer (56 years of professional experience) focused on designing, building, and deploying production-grade generative AI and agentic-AI solutions. Responsible for delivering secure, scalable, and business-oriented AI systems that operate on structured and unstructured data and enable AI-driven decision-making Build and operate scalable, reliable data pipelines on Azure. Develop batch and streaming ingestion, transform data using Databricks (PySpark/SQL), ADF, enforce data quality, and publish curated datasets for analytics and ML. Design, development, and deployment of ML solutions at scale. Drive architecture, mentor the team, and integrate advanced AI (including LLMs) into enterprise workflows Required experience 56 years of industry experience in software engineering and AI-related roles. Minimum 2-3 years of direct experience with Generative AI and Large Language Models (LLMs). Key Responsibilities: GenAI: Architect, develop, test, and deploy generative-AI solutions (online/offline LLMs, SLMs, TLMs) for domain-specific use cases. Design and implement agentic AI workflows and orchestration using frameworks such as LangGraph, Crew AI, or equivalent. Integrate enterprise knowledge bases and external data sources via vector databases and Retrieval-Augmented Generation (RAG). Build and productionize ingestion, preprocessing, indexing, and retrieval pipelines for structured and unstructured data (text, tables, documents, images). Implement fine-tuning, prompt engineering, evaluation metrics, A/B testing, and iterative model improvement cycles. Conduct/model red-teaming and vulnerability assessments of LLMs and chat systems using tools like Garak (Generative AI Red-teaming & Assessment Kit). Collaborate with MLOps/platform teams to containerize, monitor, version, and scale models (CI/CD, model registry, observability). Ensure model safety, bias mitigation, access controls, and data privacy compliance in deployed solutions. Translate business requirements into technical designs with clear performance, cost, and safety constraints. Data Engineer: Design, build, and maintain ETL/ELT pipelines in Azure Data Factory and Databricks across Bronze Silver Gold layers/Medallion Architecture. Implement Delta Lake best practices (ACID, schema evolution, MERGE/upsert, time travel, Z-ORDER). Write performant PySpark and SQL; tune jobs (partitioning, caching, join strategies). Machine Learning engineer: Machine Learning: Deep understanding of supervised, unsupervised, and reinforcement learning, model evaluation, and feature engineering. Deep Learning: Proficiency with TensorFlow, PyTorch, Keras; hands-on with CNNs, RNNs. Programming: Expert in Python (NumPy, Pandas, scikit-learn, etc.); R exposure acceptable. Required Skills and Experience: Strong proficiency in Python and experience with ML/AI libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face ecosystem). Hands-on experience with LLMs, RAG, vector databases, and retrieval pipelines. Practical experience deploying agentic workflows and building multi-step, tool-enabled agents. Experience using Garak (or similar LLM red-teaming/vulnerability scanners) to identify model weaknesses and harden deployments. Demonstrated experience implementing content filtering / moderation systems. Solid skills working with structured and unstructured data and advanced feature engineering. Familiarity with cloud GenAI platforms and services (Azure AI Services preferred; AWS/GCP acceptable). Experience building APIs/microservices; containerization (Docker), orchestration (Kubernetes). Strong understanding of model evaluation, performance profiling, inference cost optimization, and observability. Positive knowledge of security, data governance, and pri . .
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.