AI​/ML Engineer

Role Description

We are looking for an AI / ML Engineer with strong software engineering fundamentals and hands‑on experience building production‑grade Python systems, scalable data pipelines, and machine learning solutions in cloud environments. The ideal candidate will have practical experience across the machine learning lifecycle including data preparation, feature engineering, model development, evaluation, deployment, support, monitoring, and documentation.

This role is well suited for an engineer who can work at the intersection of machine learning, data engineering, and cloud‑based software development. The candidate should be comfortable developing ML‑driven automation solutions, working with structured and semi‑structured data, collaborating with cross‑functional teams, and translating research or prototype ideas into reliable engineering solutions.

• Design, develop, and maintain machine learning models and AI‑driven systems using Python and modern ML libraries.
• Build and optimize data pipelines for heterogeneous data sources such as CSV, JSON, XML, and relational databases.
• Perform data preprocessing, feature engineering, exploratory data analysis, model training, validation, and performance evaluation.
• Develop ML solutions for use cases involving classification, forecasting, embeddings, NLP, computer vision, similarity search, and intelligent automation.
• Implement scalable ETL/ELT workflows using Python, Snowflake, and cloud services such as AWS S3, Lambda, and Glue.
• Support deployment‑ready ML workflows including model monitoring, data quality checks, logging, error handling, and ingestion.
• Collaborate with data scientists, software engineers, product teams, and business stakeholders to understand requirements and deliver practical AI/ML solutions.
• Conduct experiments, compare model architectures, tune hyper‑parameters, analyze model performance, and document findings clearly.
• Develop reusable, maintainable, and well‑tested code following software engineering best practices, Git workflows, and CI/CD standards.
• Stay current with advances in machine learning, deep learning, NLP, computer vision, embeddings, and cloud‑based AI/ML platforms.

• 4 years of professional experience in software engineering, data engineering, machine learning engineering, or related technical roles.
• Strong programming experience in Python with working knowledge of SQL and familiarity with R or C as an added advantage.
• Hands‑on experience with scientific Python and ML libraries such as Num Py, Pandas, Matplotlib, scikit‑learn, Sci Py, PyTorch, Tensor Flow, Hugging Face, and transformer‑based models.
• Experience developing machine learning models using algorithms such as Random Forest, ensemble methods, deep learning models, NLP models, computer vision models, and embedding‑based retrieval systems.
• Strong understanding of data preprocessing, feature engineering, model evaluation metrics, class imbalance handling, validation techniques, and statistical testing.
• Experience designing and maintaining scalable ETL/ELT pipelines and data workflows using Snowflake, AWS, and Python.
• Working knowledge of cloud services, especially AWS services such as S3, Lambda, Glue, and cloud‑native data infrastructure.
• Experience with data quality monitoring, schema management, anomaly detection, logging, and pipeline reliability practices.
• Familiarity with Docker, CI/CD pipelines, Git‑based collaboration, technical documentation, and production software development practices.
• Ability to communicate technical concepts effectively to both technical and non‑technical stakeholders.

• Experience with MLOps concepts such as model versioning, experiment tracking, model deployment, model monitoring, and automated retraining workflows.
• Experience with multimodal AI (image‑text embeddings, semantic search, content‑based retrieval, or vector similarity search).
• Hands‑on experience with computer vision use cases including CNN‑based classification, image feature extraction, and dataset quality analysis.
• Experience with NLP use cases including BERT/Transformer fine‑tuning, speech or text…