AI/ML Architect Full Stack Developer

The candidate should be
Bachelor’s or master’s degree (preferred) in Computer Science, Engineering, or a relevant field.
Relevant architecture certifications, such as TOGAF, Zachman are good to have Cloud certifications i.e., AWS Certified Solutions Architect, Google ML Professional Cloud Architect, or Microsoft Certified: Azure Solutions Architect Expert, are a plus.
Expected Skillsets, Roles & Responsibilities:

Core Responsibilities/ Experience
Vision and aptitude for problem understanding, outcome focus, structural breakdown of problems, exhibit art of problem solving, distill business situations to analytical solutions
Experience in administering and leading data science implementations for business programs at scale in production environments
 Proven track record of driving business outcome across large scale data science driven programs across Retail, CPG, FMCG/ Healthcare/ Financial Services & Insurance/ Banking & Capital Markets/ High-Tech Manufacturing/ Digital Enterprises
 Strong analytical, problem-solving, and decision-making skills, with the ability to balance technical and business considerations.
Ability to conduct and govern AI experimentation and arrive at solution optionality and possible impact
 Excellent communication and collaboration skills, with the ability to effectively convey complex technical concepts to both technical and non-technical stakeholders.
Ability to work in a fast paced, dynamic environment and adapt to changing priorities & requirements.
In-depth fundamental and experiential knowledge in statistics, probability, machine learning, optimization techniques and application of the apt algorithm/ solution per the business problem
 Experience in deep learning techniques, their business applications
Experience in implementing statistical modelling as part of full stack developments in product deployments
 Basics of ML Ops, model monitoring, feedback and setup
Knowledge and administrative experience  in cloud computation systems (Azure, GCP, AWS) and their respective ML stack, data storage and ETL, pipelining and automation
 Basics of full stack implementation : User Story Formulation, Front End, Middleware and Backend layers, Integration, API setup and calling
 Experience working with cross-functional teams, including software developers, process experts, product managers, data engineers.

Good to have Experience

In depth knowledge in Gen AI, functionality of LLM, (Open AI, Gemini) their business applications
Knowledge in NLP, text matching, basic computer vision, visual identification & extraction, OCR techniques
In-depth creating modular, maintainable, and extensible designs that support the development of scalable, secure, and efficient software systems.
Knowledge of software architecture principles, design patterns, and best practices of full stack architectures and product developments
In-depth knowledge of end-to-end ML Ops implementation
Strong understanding of programming languages, frameworks, technologies : Python, PySpark, Java, Angular, React, Streamlit, Figma
Knowledge of microservices architecture and containerization technologies, such as Docker and Kubernetes, to enable efficient deployment and management of distributed applications
Knowledge of Agile, DevOps methodologies, tools and program administration practices
Familiarity with agile working environments, agile principles, with usage of Scrum, Kanban and other agile frameworks
Familiarity with data engineering principles, data storage, networking, database design and management, CI-CD pipelining including SQL and NoSQL databases