AI/ ML Engineer with Gen AI Applications

Job Title:

About the Role

What You Will Do

ML Engineering

• Build, train, test, validate, and deploy ML models using Python, scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, etc.
  
• Perform feature engineering, model selection, and optimization.
  
• Use techniques like transfer learning, quantization, and mixed-precision training.
  
• Create pipelines for cloud and edge devices (Jetson Nano, TensorRT, ONNX Runtime).
  
• Use proper validation methods and write clean, reproducible code.

MLOps & Automation

• Build CI/CD pipelines for ML using MLflow, Kubeflow, TFX, DVC, BentoML.
  
• Automate model versioning, retraining, and drift detection.
  
• Deploy models using Docker, Kubernetes, Terraform, or CloudFormation.
  
• Monitor models in production and ensure governance.
  
• Work with cloud ML platforms like AWS SageMaker, GCP Vertex AI, or Azure ML (preferred).
  

Data Engineering & Pipelines

• Create data pipelines for training and inference using Spark, Kafka, Airflow.
  
• Analyze data (EDA) and maintain feature stores.
  
• Use tools like dbt, Apache Beam, Great Expectations, Prefect.
  
• Work with data lakes and warehouses: Delta Lake, Iceberg, Hudi, Snowflake, BigQuery.
  
• Collaborate with data teams to ensure data quality and pipeline reliability.
  

Generative AI & GenAI

• Build LLM-based solutions using LangChain, Hugging Face Transformers, OpenAI APIs.
  
• Implement RAG (Retrieval-Augmented Generation) and optimize prompts.
  
• Fine-tune LLMs (LoRA, QLoRA, instruction tuning) and manage hallucinations.
  
• Use GenAI to enhance ML systems like recommendations, summarization, and knowledge retrieval.
  

Software & Engineering Practices

• Follow Agile processes and SDLC best practices.
  
• Use Git for version control (branching, pull requests, code reviews).
  
• Automate testing for ML code and data validation.
  
• Create high-level data and model architecture diagrams.
  
• Integrate ML pipelines into software releases.
  

Skills Required

• Programming: Python, SQL, scripting languages.
  
• ML Frameworks: scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM.
  
• MLOps: MLflow, Kubeflow, DVC, TFX, BentoML.
  
• Data Tools: Spark, Kafka, Airflow, dbt, Great Expectations.
  
• Visualization: matplotlib, seaborn, Plotly, Tableau, Power BI.
  
• Monitoring: Prometheus, Grafana, ELK.
  
• Strong understanding of statistics, probability, and causal reasoning.
  

Preferred Skills

• Databricks Lakehouse / Vertex AI Pipelines.
  
• Bayesian modeling (PyMC3, Stan), time-series forecasting (ARIMA, Prophet).
  
• Responsible AI: fairness, bias detection, explainability (SHAP, LIME).
  
• Contributions to open-source ML/AI projects or research publications.
  

Qualifications

• Bachelor’s / Master’s / PhD in Computer Science, Data Science, Statistics, or related field.
  
• 6+ years of hands-on experience in ML Engineering, MLOps, and Data Engineering.
  
• Proven experience deploying and maintaining AI models at scale.
  

Why Join Us

• Work on cutting-edge AI & GenAI projects.
  
• Innovate using open-source and enterprise tools.
  
• Collaborative and learning-focused culture.
  
• Flexible remote/hybrid options.
  
• Competitive compensation and global exposure.