- Advanced proficiency in Python with expertise in building scalable microservices using RESTful (FastAPI, Snaic) with low latency.

- Expertise in containerization technologies (Docker, Kubernetes)

- Software Engineering & Development : Strong coding practices with Python, and experience in microservices, test-driven development, and concurrency.

- DevOps & Infrastructure : Experience with CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes).

- Cloud Optimization : Familiarity with deploying AI workloads on GCP or any other cloud platforms.

- Version control and experiment tracking using Git, MLflow, and other MLOps tools for reproducibility and collaboration.

- Proficiency in scripting languages (Bash, PowerShell)

- Knowledge of agile methodologies

Domain Expertise :

- Healthcare AI Applications : Understanding of healthcare-specific data modalities, privacy constraints, and domain adaptation for clinical and operational use cases.

- Evaluation Methodologies : Proficiency in designing benchmarks, conducting human evaluations, and applying automated metrics for model performance and safety.

- Mathematical Foundations : Strong grasp of linear algebra, probability, optimization theory, and information theory relevant to deep learning and model design.

- Research Methodology : Experience in experimental design, reproducibility, statistical analysis, and peer-reviewed publication processes.

Professional Competencies :

- Strong problem-solving and analytical skills, with the ability to translate complex AI concepts into scalable engineering solutions.

- Ability to rapidly prototype and iterate?on GenAI and LLM-based applications, balancing innovation with performance and reliability.

- Effective collaboration across cross-functional teams, including data scientists, researchers, and product stakeholders, to deliver impactful AI solutions.

- Clear and concise communication skills, capable of presenting technical ideas to both technical and non-technical audiences.

- Commitment to engineering excellence, including writing clean, maintainable code, conducting thorough code reviews, and following best practices in software development.

- Proactive learning mindset, staying current with emerging trends in AI, GenAI, and agentic systems, and applying them to real-world problems.

- Experience in mentoring and knowledge sharing, supporting junior engineers and contributing to team growth and capability building.

- Ownership and accountability?in delivering high-quality solutions under tight deadlines and evolving requirements.

- Focus on reproducibility and reliability, using tools like Git, MLflow, and CI/CD pipelines to ensure consistent experimentation and deployment.

- Ethical and responsible AI development, with awareness of safety, fairness, and privacy considerations in model design and deployment