Urgent Need AI/ML Technical Architect

Hi,

Pleasure mailing you. Please go through the below requirement and let me know if you are comfortable for the position.

Please send me your updated resume along with the best hourly rate, work authorization status and availability.

An early response is really appreciated.

Job Title : AI/ML Technical Architect

Location : Remote

AIML Full Stack Architect

We are a global professional services firm that makes business transformation real. We drive digital-led innovation and digitally enabled intelligent operations for our clients, guided by our experience running thousands of processes for hundreds of Global Fortune 500 companies.

From New York to New Delhi and more than 20 countries in between, Genpact has the end-to-end expertise to connect every dot, reimagine every process, and reinvent companies’ ways of working.

Our focus is to make sure we have the right set of people delivering what we promise. People who think with design, dream in digital, and solve problems with data and analytics. People who obsess over operations, focus on the details, and lead change by being curious, incisive and courageous in everything they do—on a foundation of unyielding integrity.

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