Artificial Intelligence Engineer

0 years

0 Lacs

Posted:20 hours ago| Platform: Linkedin logo

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Work Mode

On-site

Job Type

Full Time

Job Description

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)

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