Job Description

Job Summary

We are looking for a

Senior Machine Learning Engineer / Applied Data Scientist

to design, build, and operationalize end-to-end AI/ML solutions. You will be responsible for the entire lifecycle--from data ingestion and feature engineering to model training, deployment, and automated monitoring. This role bridges the gap between experimental Data Science and production-grade Software Engineering, focusing on scalable MLOps, forecasting, and the integration of Large Language Models (LLMs) into enterprise workflows.

Key Responsibilities

Model Development & Experimentation:

Design and validate ML models (Supervised/Unsupervised, Time Series, Deep Learning). Perform EDA, engineer high-quality features, and apply statistical rigor to predictive/prescriptive analytics.

MLOps & Productionization:

Implement CI/CD for Machine Learning (ML Pipelines). Deploy models using containerization and orchestration; build robust monitoring for model drift, performance degradation, and automated retraining.

Data Engineering & Orchestration:

Design scalable batch and streaming pipelines using Spark/PySpark. Orchestrate workflows using Airflow or similar alternatives, to ensure reliable data delivery for model training and inference.

Automated Insights:

Build frameworks to automate the generation of business metrics (e.g., demand forecasting, supply chain KPIs, or threat detection) and integrate them into stakeholder-facing dashboards.

Architecture & Governance:

Apply sound software architecture and data modeling practices. Ensure all AI/ML solutions meet enterprise standards for data lineage, quality, and security compliance.

Domain Solutioning:

Partner with functional teams (Security, Retail, Supply Chain) to translate abstract business problems into concrete mathematical and engineering solutions.

Mentorship & Documentation:

Author technical designs and runbooks; mentor junior engineers to elevate the team's collective engineering standards.

Technical Skills (Non-Negotiable)

Programming & ML Frameworks:

Expert Python:

Proficiency in building production-grade packages.

ML Libraries:

Deep experience with scikit-learn and at least one Deep Learning framework (PyTorch, TensorFlow, or Keras).

Advanced Analytics:

Proven track record in modern algorithms and LLM integration.

MLOps & Infrastructure:

Platform Experience:

Hands-on with MLOps platforms such as

MLflow, Kubeflow, Databricks, or SageMaker.

DevOps for ML:

Version control (Git), CI/CD pipelines, and model registry management.

Containers:

Solid understanding of Docker and containerized deployment patterns.

Data & Big Data:

Distributed Compute:

Spark/PySpark and SQL mastery.

Data Architecture:

Familiarity with modern data lakehouse patterns and distributed storage

Good to have Skills

Orchestration:

Experience with Kubernetes (K8s) and advanced workflow tools like NiFi or Flink.

Query Engines:

High-performance engines like Trino, Presto, or Druid.

Languages:

Exposure to Scala, Java, or Rust for high-performance systems.

Specialized Domains:

Experience in Cybersecurity (threat detection) or Retail (demand forecasting).

Generative AI:

Practical experience with LLM orchestration (LangChain, LlamaIndex) and fine-tuning.

Soft Skills & Culture Fit

Statistical Rigor:

A scientific approach to deriving insights and validating experiments.

Cross-Functional Fluency:

The ability to explain "model uncertainty" to a Product Manager and "resource constraints" to a DevOps Engineer.

End-to-End Ownership:

A "Full-Stack" mindset toward the ML lifecycle--you own the model from the first line of SQL to the final API endpoint.
Job Type: Full-time

Pay: ?1,500,000.00 - ?3,000,000.00 per year

Benefits:

Health insurance Provident Fund
Work Location: On the road