This is a remote position.
Scope:
We are hiring a pioneering, fully autonomous Senior AI Engineer/Data Scientist to own the complete data science lifecycle for our flagship prediction model project. This role is mission-critical — the candidate will be the single point of expertise responsible for sourcing, analyzing, and engineering all data that powers our predictive models. Operating independently with minimal supervision, this individual must combine deep AI/ML mastery, hands-on engineering skills, and sharp business acumen to deliver measurable, production-grade outcomes.
Key Responsibilities:
Data Analysis & Pipeline Ownership
▸ Lead end-to-end analysis of large, complex, multi-source datasets to surface patterns driving model inputs
▸ Identify, collect, clean, validate, and transform all data required for prediction model consumption
▸ Design and maintain scalable, production-grade data pipelines (training, validation, inference)
▸ Perform deep EDA, data profiling, and quality audits to ensure model-ready data standards
Predictive Modeling & AI/ML
▸ Architect, train, evaluate, and iterate ML models — supervised, unsupervised, and reinforcement learning
▸ Own feature engineering: selection, extraction, transformation, and dimensionality reduction
▸ Apply advanced techniques: deep learning, NLP, time-series forecasting, ensemble methods
▸ Benchmark, A/B test, and monitor models in production; drive continuous performance improvement
▸ Deploy models via REST APIs (FastAPI/Flask); ensure reproducibility and scalability
Independent Ownership & Leadership
▸ Self-direct from problem definition through solution delivery with zero hand-holding
▸ Translate ambiguous business problems into precise, executable data science problem statements
▸ Communicate model results and data insights clearly to technical and non-technical stakeholders
▸ Document all experiments, methodologies, and outcomes — audit-ready and reproducible
▸ Champion best practices across the data science lifecycle; mentor junior team members
QUALIFICATIONS
▸ B.S./M.S./Ph.D. in Computer Science, Statistics, Mathematics, or equivalent quantitative field (Master's/Ph.D. strongly preferred)
▸ 5+ years of hands-on data science experience with at least 2 years delivering production-grade ML models
▸ Proven ability to own and deliver end-to-end data science projects independently
▸ Portfolio demonstrating innovation in predictive modeling and measurable business impact
▸ Kaggle rankings, research publications, or open-source ML contributions are a strong plus
▸ Experience in a fast-paced, data-driven, decision-model environment
REQUIRED SKILLS & QUALIFICATIONS
Core Data Science & Mathematics
▸ Statistics (Bayesian inference, hypothesis testing, regression, distributions)
▸ Linear algebra, calculus, and probability applied to ML model design
▸ Supervised & unsupervised learning, anomaly detection, clustering
▸ Time-series analysis & forecasting: ARIMA, Prophet, LSTM
Programming & Development
▸ Python (Expert): NumPy, Pandas, Scikit-learn, Statsmodels, Matplotlib, Plotly
▸ SQL (Advanced): window functions, CTEs, query optimization
▸ Git / GitHub; CI/CD for ML; MLOps with MLflow or Kubeflow
▸ Docker & Kubernetes for model containerization and serving
AI / ML Frameworks (Must-Have)
▸ TensorFlow and/or PyTorch — deep learning architectures
▸ XGBoost, LightGBM, CatBoost — gradient boosting & ensemble methods
▸ Hugging Face Transformers — NLP, LLMs, and fine-tuning
▸ SHAP, LIME — model explainability and interpretability
▸ LLMs / Generative AI / Prompt Engineering — strong advantage
Cloud & Data Infrastructure
▸ AWS (SageMaker, S3, Glue), GCP (Vertex AI, BigQuery), or Azure ML
▸ Apache Spark / PySpark — distributed data processing
▸ Airflow / Prefect — pipeline orchestration
Snowflake (Good to Have)
▸ Snowflake Data Cloud: querying, Snowpark for Python ML pipelines
▸ Snowflake Cortex AI / ML Functions for in-database ML
▸ dbt for data transformation; data governance within Snowflake
Skills Required
- Bachelor’s, master’s, or doctoral degree in Computer Science, Statistics, Mathematics, or an equivalent quantitative field
- 5+ years of hands-on data science experience
- At least 2 years delivering production-grade machine learning models
- Ability to own and deliver end-to-end data science projects independently
- Portfolio demonstrating innovation in predictive modeling and measurable business impact
- Experience in a fast-paced, data-driven, decision-model environment
- Expert-level Python with NumPy, Pandas, Scikit-learn, Statsmodels, Matplotlib, and Plotly
- Advanced SQL, including window functions, CTEs, and query optimization
- Statistics, including Bayesian inference, hypothesis testing, regression, and distributions
- Linear algebra, calculus, and probability applied to machine learning model design
- Supervised and unsupervised learning, anomaly detection, and clustering
- Time-series analysis and forecasting using ARIMA, Prophet, or LSTM
- Git or GitHub, CI/CD for machine learning, and MLOps with MLflow or Kubeflow
- Docker and Kubernetes for model containerization and serving
- TensorFlow and/or PyTorch
- XGBoost, LightGBM, and CatBoost
- Hugging Face Transformers for NLP, LLMs, and fine-tuning
- SHAP and LIME for model explainability and interpretability
- Experience with AWS SageMaker, S3, and Glue; GCP Vertex AI and BigQuery; or Azure ML
- Apache Spark or PySpark
- Airflow or Prefect
- Master’s or Ph.D. degree
- Kaggle rankings, research publications, or open-source machine learning contributions
- LLMs, generative AI, and prompt engineering
- Snowflake, Snowpark for Python ML pipelines, Snowflake Cortex AI, or ML Functions
- dbt and data governance within Snowflake
What We Do
KANINI is a digital transformation enabler, providing cutting-edge software services and solutions that help enterprises drive innovation and business growth. We create impeccable customer experiences through thoughtfully designed digital solutions that help improve our customer’s efficiency, scale, and revenues. We specialize in Product Engineering, Data Analytics & AI, and ServiceNow consultation and implementation. We focus on empowering Banking & Financial Services, Healthcare, Manufacturing, and a few other industries to leverage new-age technologies and solutions by implementing agile development practices and a global delivery framework. We build technology that puts humans first - in line with our core operating principle that technology is for people, not the other way around. The result is happier clients, partners, and employees. We do all this by balancing our customers’ needs with our people’s aspirations, always putting humans first. It means: * We prioritize each individual’s personal goals. * We value relationships more than profits. * We treat our clients as partners. Our people come to work because it makes them happy. They smile more often. They love getting better at what they do. And our offices become a second home. Even as they spend more time away from it. If you think all this is just as important to you, bring us your Energy, Intellect and Integrity. We’ll show you a happier way to work
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