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Role Summary The AI Engineer contributes to the end-to-end delivery of AI/ML solutions within the Office of Innovation, working alongside broader platform teams. This role spans model development support, GenAI application building, infrastructure automation, and governance tooling, making it an ideal position for an engineer ready to deepen expertise across the full AI delivery stack. The successful candidate is technically sharp, eager to grow, and comfortable operating in a fast-paced innovation environment within a global enterprise. Key Responsibilities AI/ML Model Development Build, train, and evaluate ML models, contributing to the full model lifecycle from data preparation through to deployment. Conduct exploratory data analysis, feature engineering, and model experimentation; document findings clearly. Support model validation, testing, and performance benchmarking activities. Generative AI & LLM Applications Develop and maintain GenAI-powered applications including chatbots, summarization tools, document processing pipelines, and internal copilots. Implement prompt engineering patterns, retrieval-augmented generation (RAG) pipelines, and tool-augmented agents using established frameworks. Participate in the evaluation of recent LLM capabilities and contribute to internal proof-of-concepts. AI Infrastructure & MLOps Support the building and maintenance of ML pipelines, including data ingestion, preprocessing, training automation, and model serving. Manage and monitor deployed models; identify and escalate performance degradation, drift, or anomalies. Contribute to infrastructure-as-code for AI workloads across cloud environments (AWS, Azure, or GCP). AI Governance & Quality Assist in producing governance documentation: model cards, data lineage records, and risk assessment inputs. Implement monitoring and logging frameworks to support auditability and compliance requirements. Apply responsible AI checklists and flag potential bias, fairness, or privacy concerns during development. Collaboration & Delivery Work closely with data engineers, platform engineers, and business analysts to integrate AI outputs into existing systems. Participate in Agile ceremonies, sprint planning, and technical design discussions. Write clean, well-documented, testable code and maintain internal technical documentation. Qualifications Essential 3 to 5 years of software engineering experience with at least 1 or 2 years in ML or AI-focused roles. Solid skills with hands-on experience in ML libraries (scikit-learn, PyTorch or TensorFlow, pandas, NumPy). Working knowledge of LLM APIs (OpenAI, Anthropic, HuggingFace) and at least one orchestration framework (LangChain, LlamaIndex). Familiarity with cloud platforms (AWS, Azure, or GCP) and containerization basics (Docker). Experience with version control (Git), basic CI/CD practices, and collaborative development workflows. Understanding of core ML concepts: supervised/unsupervised learning, model evaluation, overfitting, and feature engineering. Bachelors degree in computer science, Engineering, Mathematics, or a related field or equivalent demonstrable experience. Desirable Exposure to MLOps tools such as MLflow, DVC, or Weights & Biases. Experience with vector databases (Pinecone, Weaviate, ChromaDB, pgvector) for semantic search or RAG applications. Familiarity with responsible AI principles, bias detection methods, or model explainability tools. Cloud certifications (AWS, Azure, or GCP) at associate level or above. Experience in an enterprise IT environment (ITSM, infrastructure, networking) is a plus. .
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.