AI / ML Engineer, Systematic Equities

Alpha Alternatives; Mumbai; On-site; INR 2,000,000 - 4,200,000; Full time

Job summary

Alpha Alternatives in Mumbai seeks a specialist to advance machine learning research and develop AI applications for investment insights. You will build predictive models, evaluate methods, and collaborate with fund managers to turn research questions into actionable analysis.

The role emphasizes feature engineering, data infrastructure, model deployment, monitoring and transparent communication of results, with focus on accuracy, latency and cost considerations.

Qualifications

• Minimum 3 years of relevant experience in applied ML/AI engineering or data science.
• Bachelor’s, master’s or PhD in CS, Statistics, Mathematics, Engineering, Data Science or related quantitative discipline.
• Strong analytical and problem-solving skills with the ability to work independently and communicate clearly.

Responsibilities

• Develop predictive models using forecasting, ranking and classification techniques.
• Build AI research tools using NLP, LLMs, embeddings, retrieval-augmented generation and document intelligence.
• Develop features, design experiments and assess performance on unseen data.
• Develop data collection and processing pipelines, reusable Python components, experiment tracking and reproducible workflows.
• Support the transition of research into paper trading and production workflows, including model deployment, monitoring and maintenance.
• Collaborate with fund managers to translate investment questions into technical research and communicate findings clearly.

Skills

Applied machine learning

Statistical modeling

Model evaluation

Data analysis

Education

Bachelor’s/Master’s/PhD in CS/Statistics/Math/Engineering/Data Science

Tools

Python

Pandas

NumPy

scikit-learn

PyTorch

TensorFlow

SQL

Git

MLflow

AWS

Job description

• Machine Learning Research and Development: Develop predictive models using forecasting, ranking and classification techniques. Evaluate suitable statistical, ensemble and deep-learning methods for systematic investment applications.
• Artificial Intelligence Applications: Build and evaluate AI research tools using natural language processing, large language models, embeddings, retrieval-augmented generation and document intelligence. Extract relevant information from company filings, earnings transcripts, news and other datasets.
• Feature Engineering and Model Evaluation: Develop and select features, design experiments and assess performance on unseen data. Address data leakage, overfitting, model stability and changing data behaviour.
• Data and Research Infrastructure: Develop data collection and processing pipelines, reusable Python components, experiment tracking and reproducible research workflows.
• Implementation and Monitoring: Support the transition of research into paper trading and production workflows, including model deployment, monitoring and maintenance.
• Collaboration and Communication: Work closely with fund managers to translate investment questions into technical research. Communicate findings, model behaviour and limitations clearly through analysis and visualization.

Technical Skills

• Strong expertise in applied machine learning, statistics, feature engineering, model selection, hyperparameter tuning and rigorous model evaluation.
• Proficiency in Python, Pandas, NumPy, scikit-learn and SQL; relevant experience with PyTorch, TensorFlow, gradient-boosting libraries or comparable frameworks.
• Hands‑on experience building and evaluating AI applications using NLP, LLMs, embeddings, retrieval-augmented generation or other modern AI techniques, with attention to accuracy, reliability, latency and cost.
• Demonstrated depth in one or more areas such as time‑series forecasting, ranking, tabular ML, deep learning or NLP. Expertise in every technique is not required.
• Experience with APIs, Git, reproducible workflows and the deployment or operational use of models. MLOps, cloud platforms, data engineering and agentic AI experience are additional advantages.

Experience and Qualifications

• Minimum 3 years of relevant experience in applied ML, AI engineering or data science, with substantial hands‑on ownership of complex projects and demonstrated outcomes. Candidates from outside financial services are encouraged to apply.
• Bachelor's, master's or PhD in Computer Science, Statistics, Mathematics, Engineering, Data Science or a related quantitative discipline. Strong practical capability and project delivery will be given significant weight.
• Strong analytical and problem‑solving skills, the ability to work independently, and clear communication with technical and non-technical colleagues.
• A clear interest in transitioning into quantitative investing as a long‑term career, with a willingness to learn investment concepts and work closely with experienced investment professionals.