About the Role

We are seeking a highly skilled AI Engineer with hands-on expertise in both traditional machine learning and cutting-edge generative AI (LLMs, diffusion models, etc.), coupled with strong experience in MLOps, API development (FastAPI), and end-to-end deployment. As a key member of our AI team, you will design, develop, deploy, and optimize AI/ML solutions that power innovative products and services for real-world impact.

Key Responsibilities

• AI/ML Model Development:

Design, build, and train traditional ML models (classification, regression, clustering, etc.) and generative AI models (NLP, LLMs, image generation, etc.) using frameworks such as PyTorch, TensorFlow, scikit-learn, HuggingFace, etc.

• API & Microservices:

Build scalable and robust RESTful APIs for AI services using FastAPI (preferred) or similar frameworks. Integrate models with applications and expose inference endpoints.

• Deployment & Scaling:

Containerize applications (Docker), orchestrate with Kubernetes or similar tools, and deploy models in production environments (cloud/on-prem). Monitor, optimize, and troubleshoot AI systems post-deployment.

• Continuous Integration & Delivery (CI/CD):

Implement CI/CD workflows for automated testing, model retraining, and seamless delivery of AI features.

• Collaboration:

Work closely with data scientists, product managers, and software engineers to translate business requirements into scalable AI solutions.

• Documentation & Best Practices:

Document design decisions, code, and model performance. Champion best practices for code quality, reproducibility, and responsible AI.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
• Proven experience developing and deploying both traditional ML and generative AI models.
• Strong proficiency in Python and relevant ML/DL libraries (scikit-learn, PyTorch, TensorFlow, HuggingFace, etc.).
• Hands-on experience building REST APIs with FastAPI (or Flask/FastAPI alternatives).
• Experience with containerization (Docker) and orchestration (Kubernetes).
• Familiarity with cloud platforms (AWS, GCP, Azure) for AI/ML workloads.
• Knowledge of CI/CD tools and practices.
• Excellent problem-solving, analytical, and communication skills.

Preferred (Bonus) Skills

• Experience with generative AI (LLMs, diffusion models, prompt engineering, RAG, etc.)
• Exposure to data versioning, model monitoring, and retraining workflows.
• Knowledge of security, privacy, and responsible AI guidelines.
• Open-source contributions or published AI/ML projects.