AI / ML Engineer / Sr Engineer / Lead

Location : Kochi / Bangalore (initially in Kochi)

Role Summary

We are looking for a hands-on AI / ML Engineer to design, build, and deploy production-grade machine learning systems. The role requires strong experience in both model development and MLOps practices, ensuring that ML solutions are scalable, maintainable, and performance optimized.

Key Responsibilities

Design and implement ML models for classification, regression, clustering, Recommendation System, Anomaly Detection, Neural Networks, NLP, Comp Vision Models

Package models and deploy them into production environments using CI / CD workflows

Develop and manage ML pipelines for data preprocessing, training, testing, and serving

Monitor model performance in production (latency, drift, accuracy) and retrain when needed

Collaborate with data engineers, MLOps engineers, and DevOps to ensure smooth deployments

Support versioning of models, datasets, and configurations

Participate in performance tuning, experiment tracking, and governance enforcement

Must-Have Skills

Strong programming skills in Python, with experience in Scikit-learn, XGBoost, or TensorFlow / PyTorch

Experience building and deploying models using Flask, FastAPI, or Streamlit

Solid Grasp Of MLOps Concepts

oCI / CD for ML (GitHub Actions, Azure DevOps, etc.)

oModel versioning and registry (MLflow, SageMaker, Vertex AI, etc.)

oData versioning (DVC, LakeFS)

oMonitoring and retraining workflows

Comfortable working with containerization (Docker) and deployment on cloud platforms (AWS, Azure, or GCP)

Good understanding of model explainability, fairness, and data privacy

Nice To Have

Familiarity with Airflow, Kubeflow, or Prefect for orchestration

Exposure to feature stores and drift detection tools

Experience with NLP (LLMs, Transformers) or time series forecasting

Understanding of model caching, load testing, and API security for ML services

Domain Knowledge in Financial Services (especially Capital Markets)

Skills Required

Tensorflow, Pytorch, Gcp, MLops, Docker, XGBoost, Flask, FastAPI, Azure, Python, Azure Devops, Aws