Artificial Intelligence Engineer

Machine Learning Engineers

This position is ideal for engineers who have excelled in competitive machine learning settings such as Kaggle, possess deep modelling intuition, and can translate complex real-world problem statements into robust, well-structured ML pipelines and datasets. You will work closely with researchers and engineers to develop realistic ML problems, ensure dataset quality, and drive reproducible, high-impact experimentation

.Candidates should have 2+ years of applied ML experience or a strong record in competitive ML, and must be based in Indi

s.Responsibilit

• iesFrame unique ML problems for enhancing ML capabilities of LL
• Ms.Design, build, and optimise machine learning models for classification, prediction, NLP, recommendation, or generative tas
• ks.Run rapid experimentation cycles, evaluate model performance, and iterate continuous
• ly.Conduct advanced feature engineering and data preprocessi
• ng.Implement adversarial testing, model robustness checks, and bias evaluatio
• ns.Fine-tune, evaluate, and deploy transformer-based models where necessa
• ry.Maintain clear documentation of datasets, experiments, and model decisio
• ns.Stay updated on the latest ML research, tools, and techniques to push modelling capabilities forwa

rd.Required Qualificat

• ionsAt le

  ast 2 y

  ears of full-time experience in machine learning model develop
• mentTechnical degree in Computer Science, Electrical Engineering, Statistics, Mathematics, or a related f
• ieldDemonstrated competitive machine learning experience (Kaggle, DrivenData, or equival
• ent)Evidence of top-tier performance in ML competitions (Kaggle medals, finalist placements, leaderboard ranki
• ngs)Strong proficiency

  in Py

  th

  on, PyTorch/Tensor

  Flow, and modern ML/NLP framew
• orksSolid understanding of ML fundamentals: statistics, optimisation, model evaluation, architect
• uresExperience with distributed training, ML pipelines, and experiment trac
• kingStrong problem-solving skills and algorithmic thin
• kingExperience working with cloud environments (AWS/GCP/Az
• ure)Exceptional analytical, communication, and interpersonal sk
• illsAbility to clearly explain modelling decisions, tradeoffs, and evaluation res
• ultsFluency in Eng

lishPreferred / Nice to

• HaveKa

  ggle Grandm

  as

  ter, M

  aster, or mult

  iple Gold M

• edalsExperience creating benchmarks, evaluations, or ML challenge pro
• blemsBackground in generative models, LLMs, or multimodal lea
• rningExperience with large-scale distributed tra
• iningPrior experience in AI research, ML platforms, or infrastructure
• teamsContributions to technical blogs, open-source projects, or research publica
• tionsPrior mentorship or technical leadership exper
• iencePublished research papers (conference or jou
• rnal)Experience with LLM fine-tuning, vector databases, or generative AI work
• flowsFamiliarity with MLOps tools: Weights & Biases, MLflow, Airflow, Docker,
• etc.Experience optimising inference performance and deploying models at

scaleWh

• y JoinGain exposure to cutting-edge AI research workflows, collaborating closely with data scientists, ML engineers, and research leaders shaping next-generation AI sy
• stems.Work on high-impact machine learning challenges while experimenting with advanced modelling strategies, new analytical methods, and competition-grade validation techn
• iques.Collaborate with world-class AI labs and technical teams operating at the frontier of forecasting, experimentation, tabular ML, and multimodal anal
• ytics.Flexible engagement opt

  ions (30–40 hrs/week or ful

  l-time) — ideal for ML engineers eager to apply Kaggle-level problem solving to real-world, production-grade AI sy
• stems.Fully remote and globally flexible — optimised for deep technical work, async collaboration, and high-output research environ

ments.We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon re