Senior Software Engineer - AI

Requirement:

• 4+ years of experience in software development, with a strong focus on full-stack and cloud technologies.
• Expertise in AI/ML engineering, specifically vector embeddings, prompt engineering, and context engineering.
• Strong programming skills in at least two of the following: Python.
• Hands on experience into Pyspark.
• Proficiency in deploying solutions on Azure Cloud.
• Experience with databases such as Azure AI Search, Redis, and Cosmos DB (Blob Storage and Iceberg are plus).
• Proven ability to design and manage Azure Functions and Azure Container Apps.
• Strong understanding of cloud-native architecture, scalability, and performance optimization.
• Design and implement AI-driven solutions that align with business objectives.
• Expertise in AI/ML frameworks like TensorFlow and PyTorch, Agentic frameworks like LlamaIndex, LangChain
• Develop AI models, systems and infrastructure, including GenAI tools (e.g. GPT, GANs) NLP tools (SpaCy, NLTK, Hugging Face), ML and DL tools.
• Proficiency and hands-on experience in programming languages such as Python, R, Java, or C++.
• Collaborating with data scientists, engineers, and business stakeholders.
• Guide technology selection, ensuring solutions align with business goals and compliance standards.
• Developing scalable AI architectures and ensuring seamless integration with existing systems.
• Overseeing AI model deployment, optimization, and performance monitoring.
• Ensuring compliance with ethical AI guidelines and data privacy regulations.
• Experience with cloud platforms (AWS, Azure, or Google Cloud).
• Strong knowledge of DevOps, MLOps, and CI/CD pipelines.
• Understanding of big data technologies and analytics is preferable
• RDBMS (Oracle., SQL server or DB2)
• NoSQL (Mongo/docDb)
• Health care domain knowledge is preferable
• Excellent problem-solving skills and ability to think critically.
• Solid understanding of software development methodologies such as Agile and Scrum.

Job Responsibility:

• Strategic Planning Developing AI strategies that integrate with enterprise goals and identifying opportunities for AI adoption.
• Solution Architecture Design scalable AI frameworks, ensuring seamless integration with enterprise systems.
• Architecture & Development: Overseeing the development of scalable, high-performing applications requires a strategic approach, regardless of the tech stack.
• Code Quality & Reviews: Conduct code reviews and enforce best practices for clean, maintainable code.
• Collaboration – Working closely with data scientists, engineers, and business stakeholders to ensure AI solutions meet organizational needs.
• Model Deployment & Optimization – Overseeing AI model implementation, monitoring performance, and refining algorithms for efficiency.
• Security & Compliance – Ensuring AI solutions adhere to ethical guidelines, data privacy regulations, and cybersecurity best practices.
• Integration & Deployment: Ensure seamless integration of front-end and back-end components.
• Continuous Improvement – Establishing feedback loops to enhance AI models and maintain system reliability.
• Mentorship: Guide junior developers, providing technical support and career development.
• Innovation & Research.