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Location: Noida - Hybrid/Remote Experience: Typically 3+ years in machine learning, applied AI, research engineering, or equivalent technical work Education: B.Tech. / M.Tech. / MS in Computer Science or related technical disciplines. About RocketFrog.ai: RocketFrog.ai is an AI Studio for Business focused on translating advances in artificial intelligence into systems that create measurable real-world impact. We work across Agentic AI, deep learning, multimodal systems, and AI-first product development for industries such as Healthcare, Pharma, Banking, Finance, Insurance, and Hi-Tech. The Opportunity: We are looking for a curious and execution-oriented Machine Learning and Applied AI Engineer to join our AI team. This role combines a research mindset with strong engineering judgment. You will work on open-ended problems where the correct formulation or technical approach may not be known in advance. You will reason from first principles, test hypotheses, challenge weak assumptions, and independently own substantial workstreams from experimentation through production. We care more about how you think, learn, and execute than whether you match a specific career path or technology stack. What You Will Own: Translate ambiguous business and technical challenges into well-defined machine learning problems.Identify important assumptions, constraints, failure modes, and evaluation criteria.Design, train, fine-tune, and evaluate models using strong baselines and structured experiments.Conduct error analysis, ablations, and robustness testing to determine whether improvements are genuine.Build scalable and reproducible systems across data preparation, training, deployment, and monitoring.Translate research ideas into reliable production implementations.Evaluate trade-offs involving model quality, latency, cost, safety, and usability in collaboration with senior team members.Own defined problems or workstreams after deployment and iterate based on real-world system behavior.Communicate technical decisions, findings, uncertainties, and limitations clearly.Contribute reusable infrastructure, documentation, and improvements to team practices. What We Value First-Principles Thinking and Research Mindset: You break problems down to their fundamentals rather than applying tools mechanically. You question assumptions, read research critically, form testable hypotheses, and update your views when evidence contradicts intuition. Critical Problem Solving: You are comfortable working through ambiguity and learning unfamiliar domains. You can distinguish meaningful results from noise and identify weaknesses in data, assumptions, or evaluation methods. Ownership: You take responsibility for the quality and impact of your work. You follow problems beyond model development and collaborate effectively when broader product or system decisions are required. Intellectual Rigor and Execution: You actively look for failure modes and edge cases. You can prototype quickly while maintaining reproducibility, experimental discipline, and sound engineering practices. Technical Foundations: Strong candidates will demonstrate depth in one or more of the following areas. We do not expect experience with every technology or domain. Mathematics and machine learning fundamentalsFoundation models across language, vision, speech, or multimodal AIFine-tuning, transfer learning, or model adaptationExperimental design and trustworthy model evaluationDataset preparation and data-quality analysisClean and maintainable Python developmentModern ML frameworks such as PyTorchAI-assisted development tools such as Claude Code or CodexProduction model serving, deployment, and monitoringModel efficiency and inference optimization Areas That May Be Relevant: Depending on the problem, your work may involve: Large language models and multimodal systemsRetrieval, embeddings, ranking, and RAGAgentic systems involving tool use, planning, or orchestrationComputer vision, speech, document intelligence, or time-series modelingSynthetic data, model optimization, and human evaluationAI safety, reliability, explainability, and privacyCloud or on-premises AI infrastructureExperience in one or two of these areas is sufficient. We value depth, sound reasoning, and the ability to learn unfamiliar systems more than broad but shallow exposure. You May Be a Strong Fit If You: Are energized by difficult and poorly specified problems.Ask why before asking which framework to use.Move comfortably between experimentation, engineering, and product considerations.Prefer evidence and careful analysis over intuition alone.Challenge assumptions constructively, including your own.Can independently own a meaningful technical workstream.Make progress under uncertainty and know when to seek guidance.Care about both scientific validity and real-world usefulness.Learn unfamiliar concepts and technologies quickly.Contribute .
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.