AI - ML Engineer

Job Summary

AI/ML Engineer

Key Responsibilities

• Design, develop, and deploy

  end-to-end AI/ML pipelines from data ingestion and preprocessing to model training, validation, and deployment.

• Collaborate with cross-functional teams

  (data engineers, backend developers, and domain experts) to understand business requirements and translate them into scalable ML solutions.

• Build and optimize machine learning models

  using supervised, unsupervised, and deep learning techniques.

• Perform feature engineering

  and exploratory data analysis (EDA) to derive insights and improve model performance.

• Integrate ML models into production environments

  using REST APIs, Flask/FastAPI, or via containerization (Docker/Kubernetes).

• Monitor model performance

  post-deployment and perform retraining, tuning, and maintenance as needed.

• Apply MLOps principles

  to automate the ML lifecycle (CI/CD, versioning, monitoring).
• Work with

  cloud platforms (AWS / GCP / Azure)

  for data storage, training, and deployment.

• Document

  model design, architecture, and performance metrics clearly for reproducibility and audit purposes Stay updated with the

  latest AI and ML research

  , frameworks, and best practices.

Required Skills and Qualifications

• Bachelors/Masters Degree

  in Computer Science, Data Science, Artificial Intelligence, or a related field.

• 3+ years of industry experience

  in machine learning or AI model development.
• Strong proficiency in

  Python

  and ML libraries such as

  scikit-learn, TensorFlow, PyTorch, Keras, Pandas, NumPy

  .
• Experience with

  data preprocessing

  ,

  feature engineering

  , and

  EDA

  using large and complex datasets.
• Hands-on experience in

  developing and deploying ML models

  using frameworks such as

  Flask, FastAPI

  , or

  Streamlit

  .
• Experience with

  model versioning, retraining, and pipeline automation

  using

  MLflow, Kubeflow, or Airflow

  .
• Proficiency in

  SQL/NoSQL databases

  and experience with data pipelines (ETL).
• Familiarity with

  containerization and orchestration

  tools like

  Docker and Kubernetes

  .
• Understanding of

  cloud AI services

  (AWS Sagemaker, GCP Vertex AI, Azure ML).
• Strong understanding of

  mathematics and statistics

  , including probability, optimization, and linear algebra.
• Experience with

  Git, CI/CD pipelines

  , and

  API integration

  .

Preferred / Nice-to-Have Skills

• Experience with NLP, Computer Vision, or Time Series Forecasting.
• Familiarity with LLM (Large Language Models) and fine-tuning open-source models (e.g., Hugging Face).
• Exposure to Big Data tools like Spark, Hadoop, or Kafka.
• Knowledge of data privacy, compliance, and model explainability (XAI).
• Experience in AI model evaluation metrics, bias detection, and fairness in AI.

Key Achievements (Example for Candidate Profile / Resume)

• Successfully designed and deployed

  end-to-end AI/ML pipelines

  integrating data ingestion, preprocessing, model training, and API-based deployment.
• Implemented

  real-time inference system

  using

  Kafka + Flask API + Dockerized microservices

  .
• Improved model accuracy and latency by

  optimizing feature sets and applying hyperparameter tuning

  .
• Automated retraining workflow using

  MLflow and Airflow

  , reducing manual intervention by 70%.
• Deployed scalable ML models on

  AWS/GCP

  with auto-scaling and monitoring dashboards using

  Prometheus/Grafana

  .