AI/ML Architect Full Stack Developer Full-time

Role : AI/ML Architect Full Stack Developer

Type : contract

Inviting applications for the role of AIML Technical Architect – AI/ML Practice.

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