Design and implement AI/Gen AI applications, systems, and infrastructure across major clouds (Azure, AWS and GCP)
Collaborate with data engineers & build AI/ML models.
Collaborate with clients & stakeholders to translate business and functional requirements into robust, scalable, operable solutions.
Participate in architectural decisions, contribute to the development of architectures for industry-wide use cases, and design architectures for AI/ML and Generative AI
Set the standards for engineering best practices.
Identify opportunities where generative AI can provide value and solve business problems.
Responsible for creating POCs and POVs across horizontals and industry use cases.
Write high-quality, efficient, testable code in Python and other languages.
Stay up to date with the latest AI trends and evaluate state-of-the-art AI technologies/framework to drive innovation.
Facilitate design and architecture workshops and mentor AI engineers in coding best practices and problem-solving.
Qualifications:
A minimum of 3-5 years in building and deploying AI and Gen AI solutions on-premises or on cloud platforms.
Bachelor’s or master’s degree in computer science, Data Science, Engineering, or related field.
Hand-on experience in deploying AI/ML solutions on different cloud platforms like Azure, AWS and/or Google Cloud.
Experience in using and orchestration LLM models on cloud platforms i.e., OpenAI @ Azure/AWS Bedrock/ GCP Vertex AI or Gemini AI
Experience in Agentic frameworks building and deploying (e.g. Semantic Kernel, CrewAI, LangGraph etc.)
Experience in writing SQL and data modelling.
Experience in designing and implementation of AI solution using microservice based architecture.
Understanding of machine learning, deep learning, NLP and GenAI.
Strong programming skills in Python and/or pyspark.
Proven experience in integrating authentication security measures within machine learning operations and applications.
Excellent problem-solving skills and ability to connect AI capabilities to business value.
Strong communication and presentation skills.
Proven experience in AI/ML solution deployment process on Kubernetes, Web Apps, Databricks or on similar platforms.
Familiarity with MLOps concepts and tech stack. Good to know code versioning, MLFlow, batch prediction and real-time end point workflows.
Familiarity with Azure DevOps / GitHub actions/ Jenkins / Terraform / AWS CFT etc.
Familiarity with Responsible AI concepts
Other Responsibilities:
Manage end to end project delivery- scoping, solutioning, designing, developing, testing, deploying or maintaining.
Ensuring the solutions meet the client requirements and expectations.
Communicate effectively with the clients and internal stakeholders throughout the project.
Identify new opportunities and generate proposals for potential clients.
Present the solutions and showcase the value proposition to senior stakeholders.