🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
Primary Responsibilities - Translate business problems into AI/ML, Generative AI, and Agentic AI solution approaches - Conduct hands-on experimentation using machine learning, Generative AI, Agentic AI, and emerging AI technologies - Design, build, and validate proof-of-concepts (POCs) and prototypes to assess technical feasibility, business value, scalability, and operational readiness - Develop production-oriented POCs that establish implementation patterns, reusable assets, architecture guidance, deployment approaches, and operational considerations required for enterprise adoption - Create reusable prompts, workflows, evaluation frameworks, reference architectures, solution accelerators, and implementation assets for broader organizational adoption - Drive successful transition of validated POCs into production by partnering closely with engineering teams to ensure solutions are scalable, maintainable, secure, and aligned with enterprise architecture standards - Develop implementation-ready artifacts including reusable code components, prompt libraries, workflow templates, deployment recommendations, evaluation methodologies, and technical documentation to accelerate engineering adoption - Own the technical readiness of AI solutions by proactively identifying scalability constraints, operational dependencies, implementation risks, and mitigation strategies during experimentation - Apply AI Development Lifecycle (AIDLC) practices during experimentation phases, including: - Structured evaluation and benchmarking - Iterative model refinement - Experiment tracking and documentation - Performance and cost optimization - Document learnings, experimentation results, architectural recommendations, and reusable solution assets - Develop Generative AI solutions leveraging: - Retrieval-Augmented Generation (RAG) architectures - Prompt engineering and optimization techniques - Vector databases and semantic retrieval frameworks - AI evaluation and guardrails - Build and evaluate Agentic AI workflows, including: - Tool integration and orchestration - Multi-step reasoning and planning - Multi-agent collaboration patterns - Autonomous and semi-autonomous workflows - Evaluate emerging AI frameworks, platforms, and technology stacks to identify opportunities for innovation, standardization, and enterprise adoption - Support development and adoption of AI accelerators, reusable frameworks, and best practices across teams - Optimize early-stage solution cost efficiency through: - Token usage awareness and optimization - Prompt tuning and response management - Model selection based on use-case requirements and cost-performance targets - Cost-performance tradeoff analysis - Collaborate with business, product, architecture, and engineering teams to clarify requirements and align solutions with measurable business outcomes - Communicate experimentation results, trade-offs, recommendations, implementation considerations, and business impact to technical and non-technical stakeholders - Accelerate organizational AI adoption by reducing the cycle time from experimentation to production deployment through repeatable patterns and reusable assets - Measure success through: - Quality and business impact of AI/ML, GenAI, and Agentic AI POCs - Production readiness of delivered solutions - Percentage of POCs successfully adopted and deployed into production - Adoption of reusable accelerators, prompts, workflows, and reference architectures - Reduction in experimentation-to-production cycle time - Delivery of measurable business outcomes enabled through productionized AI solutions - Collaborate with research, engineering, and product teams to translate cutting-edge AI advancements into production-ready capabilities. Uphold ethical AI principles by embedding fairness, transparency, and accountability throughout the model development lifecycle - Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work advantages and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so Requirements - Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Artificial Intelligence, or related field; Master's degree preferred - 5+ years of experience delivering AI/ML solutions with strong ownership of enterprise-scale AI initiatives - Experience translating business challenges into effective AI/ML solution strategies .
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.