Python Fullstack Developer

• 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

Technical Skills:

• 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, Helm).
• 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: