Job Title: AI/ML Engineer (3+ Years Experience)

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

·         Bachelor’s/Master’s 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, Kera’s, 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 .

Job Type: Full-time

Experience Level: Mid-level (3–5 years)

Work Location: Onsite

Salary Range: As per industry standards