Machine Learning Engineer

Machine Learning Engineer

We are seeking a talented and innovative Machine Learning Engineer to design, develop, deploy, and maintain machine learning models and AI-powered solutions. The ideal candidate will work closely with data scientists, software engineers, and business stakeholders to build scalable machine learning systems that solve complex business problems and drive data-driven decision-making.

Key Responsibilities:

• Design, develop, train, and deploy machine learning models and algorithms.
• Build scalable data pipelines for data collection, processing, and feature engineering.
• Evaluate and optimize machine learning models for accuracy, performance, and scalability.
• Collaborate with cross-functional teams to understand business requirements and translate them into AI/ML solutions.
• Deploy and monitor machine learning models in production environments.
• Perform model validation, testing, and performance tuning.
• Develop and maintain MLOps workflows for continuous integration and deployment of ML models.
• Analyze large datasets to identify trends, patterns, and opportunities for predictive analytics.
• Research and implement emerging AI and machine learning technologies.
• Document model architectures, processes, and technical solutions.

Required Skills:

• Strong understanding of Machine Learning, Deep Learning, and Artificial Intelligence concepts.
• Experience with supervised and unsupervised learning algorithms.
• Knowledge of data preprocessing, feature engineering, and model evaluation techniques.
• Strong analytical and problem-solving abilities.
• Experience deploying machine learning solutions into production.
• Excellent communication and collaboration skills.

Technical Skills:

• Programming Languages: Python, R, Java
• Machine Learning Libraries: Scikit-learn, TensorFlow, PyTorch, Keras
• Data Processing: Pandas, NumPy, Apache Spark
• Databases: MySQL, PostgreSQL, MongoDB
• Cloud Platforms: AWS, Azure, Google Cloud Platform (GCP)
• MLOps Tools: MLflow, Kubeflow, Airflow
• Version Control: Git, GitHub, GitLab
• Containerization: Docker, Kubernetes
• Data Visualization: Matplotlib, Seaborn, Power BI, Tableau

Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related field.
• Relevant AI/ML certifications are a plus.

Experience:

• 3–7 years of experience in Machine Learning, Data Science, or AI-related roles.
• Hands-on experience building and deploying machine learning models in production environments.
• Experience working with large-scale datasets and distributed computing frameworks.
• Familiarity with Agile development methodologies.

Preferred Qualifications:

• Experience with Generative AI, Large Language Models (LLMs), and Natural Language Processing (NLP).
• Knowledge of Computer Vision and Deep Learning frameworks.
• Experience with Retrieval-Augmented Generation (RAG) architectures.
• Familiarity with vector databases such as Pinecone, Weaviate, or ChromaDB.
• Experience integrating AI services through APIs and cloud-based AI platforms.

Preferred Qualities:

• Strong problem-solving and critical-thinking skills.
• Passion for innovation and emerging AI technologies.
• Ability to work independently and in a collaborative environment.
• Excellent communication and presentation skills.
• Commitment to continuous learning and professional development.

Employment Type:

Full-Time

Location:

Remote / Hybrid / On-site (based on company requirements)

Nice to Have:

• Experience with Generative AI tools such as OpenAI APIs, Claude, Gemini, or similar platforms.
• Knowledge of LLM fine-tuning, prompt engineering, and AI model evaluation.
• Experience building AI-powered chatbots, recommendation engines, or predictive analytics solutions.
• Contributions to open-source AI/ML projects or published research in machine learning.