Role Overview
We are looking for a passionate AI Engineer who combines strong AI/ML foundations with a solid understanding of infrastructure, system design, and production environments. The ideal candidate is curious, hands-on, and takes ownership someone who continuously experiments, learns, and improves.
This role goes beyond model building. You will contribute to designing scalable AI systems, provisioning infrastructure, and deploying reliable generative AI solutions into real-world environments.
Key Responsibilities
1. AI & Generative AI Development
- Design, build, and evaluate machine learning models with emphasis on Generative AI (LLMs, diffusion models, embeddings, RAG pipelines).
- Experiment with state-of-the-art generative techniques and translate research into production-ready features.
- Optimize prompts, fine-tuning strategies, and inference performance.
- Evaluate models using quantitative and qualitative metrics aligned with business goals.
2. Infrastructure & Provisioning
- Understand and support provisioning of AI workloads (compute, storage, networking).
- Work with cloud services and GPU environments for training and inference workloads.
- Assist in designing scalable and cost-efficient AI infrastructure.
- Contribute to CI/CD pipelines for ML models (MLOps mindset).
3. System Design & Integration
- Participate in designing AI systems that are scalable, reliable, and maintainable.
- Understand trade-offs between latency, cost, performance, and scalability.
- Integrate AI components into production systems and client platforms.
- Collaborate on API design and microservices architecture for AI services.
4. Data Engineering & Model Lifecycle
- Collect, preprocess, and validate high-quality datasets.
- Support data pipelines and feature engineering processes.
- Monitor model performance in production and contribute to continuous improvement.
5. Ownership & Growth Mindset
- Take end-to-end ownership of assigned features or experiments.
- Proactively identify improvement opportunities in models, systems, and workflows.
- Continuously explore new tools, frameworks, and research.
- Share learnings and insights with the team to elevate collective expertise.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Data Science, or related field.
- Strong understanding of machine learning fundamentals (supervised/unsupervised learning, neural networks, model evaluation).
- Foundational knowledge of Generative AI concepts (LLMs, embeddings, transformers, prompt engineering, RAG).
- Proficiency in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow, or similar).
- Basic understanding of system design principles (APIs, scalability, distributed systems fundamentals).
- Familiarity with cloud environments and infrastructure concepts (compute, storage, networking basics).
- Experience working on AI projects (academic, internship, or personal).
- Strong problem-solving ability and analytical thinking.
- Demonstrated eagerness to learn, experiment, and adapt to new technologies.
- Clear communication skills and collaborative mindset.
Preferred Qualifications
- Experience deploying ML models into production environments.
- Exposure to MLOps tools (Docker, Kubernetes, CI/CD, model monitoring).
- Hands-on experience with cloud platforms (AWS, Azure, or GCP).
- Experience working with large language models, fine-tuning, or vector databases.
- Understanding of cost optimization for AI workloads.
- Contributions to open-source projects or AI research initiatives.
