? 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 significant 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 fundamentals

Foundation models across language, vision, speech, or multimodal AI

Fine-tuning, transfer learning, or model adaptation

Experimental design and trustworthy model evaluation

Dataset preparation and data-quality analysis

Clean and maintainable Python development

Modern ML frameworks such as PyTorch

AI-assisted development tools such as Claude Code or Codex

Production model serving, deployment, and monitoring

Model efficiency and inference optimization Areas That May Be Relevant:

Depending on the problem, your work may involve:





Large language models and multimodal systems

Retrieval, embeddings, ranking, and RAG

Agentic systems involving tool use, planning, or orchestration

Computer vision, speech, document intelligence, or time-series modeling

Synthetic data, model optimization, and human evaluation

AI safety, reliability, explainability, and privacy

Cloud or on-premises AI infrastructure

Experience 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 positively to technical reviews, shared learning, and engineering practices.

What Success Looks

Like

In this role, success means:

Problems are framed more clearly because of your contribution.

Experiments produce trustworthy conclusions rather than only better metrics.

Research ideas are converted into useful and reliable systems.

Technical decisions are supported by explicit assumptions and evidence.

Models are evaluated under realistic operating conditions.

Failures are identified, understood, and addressed systematically.

You independently deliver substantial workstreams while collaborating effectively with the wider team.

Your work creates measurable value for customers and contributes to RocketFrog.ai’s technical capabilities. ? Ready to take a Rocket Leap with Science?

We are building a team of people who question assumptions, investigate difficult problems deeply, and turn ambitious ideas into reliable products.

📌 Machine Learning & Applied AI Engineer (Noida)
🏢 RocketFrog.ai
📍 Noida