About the role

Our client is seeking a highly motivated AI/ML Engineer (2–4 years) with hands-on experience in developing machine learning models, building data pipelines, and integrating ML solutions into production systems. You will collaborate with engineering, data, and product teams to deliver intelligent, scalable AI solutions across multiple domains.

This role is ideal for someone who has strong foundational ML knowledge, exposure to deep learning/LLMs, and is ready to work on end-to-end ML lifecycle projects

Key Responsibilities

Develop, train, and optimize machine learning and deep learning models for real-world applications.
Build, maintain, and improve ML pipelines including data preprocessing, feature engineering, and model deployment.
Implement ML algorithms using Python, scikit-learn, TensorFlow, PyTorch, or related frameworks.
Work with large datasets to design efficient ETL workflows and ensure training data quality.
Deploy ML models into production using APIs, microservices, or cloud-native services.
Participate in model evaluation, A/B testing, drift monitoring, and continuous performance improvements.
Collaborate with engineering teams to integrate ML models within existing systems.
Stay updated with recent research, tools, and best practices in ML, DL, NLP, and LLMs.
Write clean, maintainable code following engineering best practices.

Required Skills

2–4 years of hands-on experience in Machine Learning or applied AI development.
Strong proficiency in Python and ML libraries (scikit-learn, NumPy, Pandas).
Solid understanding of supervised & unsupervised learning algorithms.
Experience with frameworks such as TensorFlow or PyTorch.
Basic exposure to NLP or LLM-based models is preferred.
Knowledge of building and deploying model pipelines.
Familiarity with data pipelines, ETL processes, and handling structured/unstructured data.
Experience working with APIs, microservices, or ML model deployment workflows.
Good understanding of Git, version control, and collaborative coding.

Preferred Skills (Good to Have)

Experience with cloud ML platforms (AWS Sagemaker, Azure ML, GCP Vertex AI).
Basic understanding of vector databases and retrieval systems.
Exposure to MLOps tools: MLflow, Kubeflow, Airflow, or DVC.
Experience with Docker and containerized deployments.
Familiarity with LLM fine-tuning, embeddings, or RAG pipelines.
Hands-on experience with SQL/NoSQL databases.

Soft skill

Strong analytical and problem-solving abilities.
Ability to break down ambiguous problems into structured ML tasks.
Good communication skills for cross-functional collaboration.
Curiosity and willingness to learn new technologies.
Team player with a proactive attitude.

Must have skills

Strong proficiency in Python and ML libraries (NumPy, Pandas, scikit-learn)
Solid understanding of machine learning algorithms, feature engineering, and model evaluation
Hands-on experience with deep learning frameworks (PyTorch or TensorFlow)
Experience in data preprocessing, working with large datasets, and SQL
Ability to build, deploy, and serve ML models using REST APIs (FastAPI/Flask)
Working knowledge of Docker and basic cloud platforms (AWS/GCP/Azure)
Familiarity with MLOps practices (model versioning, experiment tracking, monitoring)
Proficiency with Git and collaborative development workflows
Strong problem-solving, communication, and documentation skills

About YMinds.AI

YMinds.AI is a premier talent solutions company specializing in identifying and placing top-tier engineering and technology leaders with high-growth startups and global enterprises. We partner closely with our clients to deliver senior, high-impact talent that drives architectural excellence, innovation, scalability, and long-term business success.

Hashtags

#AIMLEngineer #MachineLearningEngineer #MachineLearning #DeepLearning #NLP #LLM #TensorFlow #PyTorch #ScikitLearn #DataEngineering #MLOps #AIEngineer #PythonDeveloper #ArtificialIntelligence #AIJobs #TechHiring

Required Skills

Machine Learning: Very Important

Python: Very Important

TensorFlow: Very Important

PyTorch: Very Important

Data Engineering: Important

Natural Language Processing (NLP): Important

ETL Processes: Important

APIs (REST, GraphQL): Important

Docker: Important

MLOps: Important

SQL: Important

Git: Important

Qualifications

[Some qualifications you may want to include are Skills, Education, Experience, or Certifications.]

Example: Excellent verbal and written communication skills

AI/ML Engineer (2-4 years Experience)