Fullstack AI/ML Engineer

Responsibilities

Design & Develop AI Solutions:

Identify AI Opportunities:

Model Implementation & Optimization:

Data Management & Engineering:

Backend Integration:

Performance Monitoring & Improvement:

Research & Innovation:

Testing & Deployment:

Qualifications

Required Skills & Qualifications:

• Experience: 2+ years of professional experience in Artificial Intelligence, Machine Learning, or Data Science roles, with a proven track record of delivering production-grade AI/ML solutions (or equivalent demonstrable expertise).
• Technical Expertise:
• Proficiency in Python and strong experience with core AI/ML libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers).
• Solid grasp of various machine learning algorithms (supervised, unsupervised, reinforcement learning) and deep learning architectures.
• Demonstrated experience applying machine learning to complex datasets, including structured and unstructured data.
• Proficient in API design (REST, GraphQL), microservices, and database design (SQL/NoSQL); production experience on at least one major cloud (AWS, Azure, or GCP).
• Practical knowledge of Docker, Kubernetes, and CI/CD pipelines (GitHub Actions, Argo, or similar).
• Problem-Solving: Excellent analytical and problem-solving skills, with proven ability to break down complex problems into iterative experiments and devise effective, scalable AI/ML solutions
• Enthusiasm & Learning: A genuine passion for technology, coupled with a self-driven commitment to continuous learning and mastery of new techniques. We value individuals who proactively identify challenges, conceptualize solutions, and lead ideation and innovation, beyond mere task execution
• Communication: Strong communication skills to articulate complex technical concepts to both technical and non-technical stakeholders.
• Education: Bachelor's or master's degree in computer science, Artificial Intelligence, Machine Learning, or a related quantitative field.
  

Bonus Points:

• Domain expertise in EV charging, smart-grid, or energy-management systems.
• Experience with distributed data technologies (Spark, Flink, Kafka Streams).
• Contributions to open-source ML projects or peer-reviewed publications.
• Knowledge of ethical and responsible AI frameworks, including bias detection and model explainability.