AI/ML Engineer

Job Summary

We are seeking a skilled AI/ML Engineer with at least 3 years of hands-on experience in designing, developing, and deploying end-to-end machine learning solutions. The ideal candidate will have practical exposure to data preprocessing, model development, model evaluation, deployment (MLOps), and integration with production systems. You should have a strong foundation in Python, machine learning frameworks, and cloud-based deployment environments.

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/Fast API, 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, Fast API, or Streamlet.
• Experience with model versioning, retraining, and pipeline automation using Moldflow, 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 Sage maker, 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 + Dockized microservices.
• Improved model accuracy and latency by optimizing feature sets and applying hyperparameter tuning.
• Automated retraining workflow using ML flow and Airflow, reducing manual intervention by 70%.
• Deployed scalable ML models on AWS/GCP with auto-scaling and monitoring dashboards using Prometheus/Grafana.