AI/ML Developer

Design and develop scalable AI/ML solutions using LLMs and other machine learning models.

Architect and implement Generative AI systems that are scalable, resilient, and aligned with ethical AI practices.

Work as a Subject Matter Expert (SME) for Generative AI, partnering with Product and Engineering teams.

Develop and maintain end-to-end AI pipelines, including:

• Data preprocessing
• Feature engineering
• Model training and evaluation

Build extensible APIs and integrations to connect AI models with enterprise systems.

Develop low-code/no-code UI/UX solutions for rapid delivery cycles.

Design structured outputs (JSON, arrays, HTML) with nested nodes, ensuring seamless frontend/dashboard consumption.

Implement solutions integrating LLMs (e.g., GPT models) to extract insights and deliver actionable data.

Architect and deploy solutions on cloud platforms such as AWS, Azure, or GCP.

Build and maintain distributed, scalable systems for AI workloads.

Optimize models and systems for performance, scalability, and efficiency.

Optimize generative AI models for improved accuracy and response quality.

Develop effective prompt engineering strategies based on understanding how AI interprets data.

• Retrieval-Augmented Generation (RAG)
• Conversational systems
• Summarization and translation

Design solutions aligned with Responsible AI principles:

• Fairness
• Transparency
• Security
• Accountability

Identify, assess, and mitigate risks associated with generative AI systems.

Ensure compliance with privacy and data protection standards, especially in healthcare environments.

Collaborate closely with cross-functional teams including Product, Data, and Engineering.

Translate business problems into actionable AI-driven solutions.

• Technical specifications
• Architecture diagrams
• User guides and presentations

Contribute to best practices and standards for AI/ML development across the organization.

Proven experience delivering at least one large-scale Generative AI solution.

• Machine Learning & AI (especially NLP and Generative AI)

• Frameworks: TensorFlow, PyTorch

• Open-source platforms: Hugging Face

• LLMs (GPT family, DALL·E, or similar)

• Data pipelines and ML lifecycle management

Strong software engineering skills for deploying AI into production environments.

Experience building APIs, system integrations, and scalable architectures.

Solid understanding of distributed systems design.

Experience in healthcare domain or regulated environments.

• RAG architectures

• AI-powered dashboards and visualization tools

Familiarity with CI/CD and DevOps practices.

Experience designing enterprise-grade, production-ready AI systems.

Tools: Hugging Face, APIs, data pipelines

Data formats: JSON, HTML, structured outputs

Strong analytical and problem-solving abilities

Excellent communication skills (technical and non-technical audiences)

Ability to work in fast-paced, agile environments

Strategic thinking and solution-oriented mindset