Practice Lead - Data Science

at  Infosys

What are we looking for? Lead AI Engineer.
We are looking for smart, self-driven, high-energy people with top notch communication skills, intellectual curiosity and passion for technology in the Machine Learning Space. Our analysts have a blend of in-depth domain expertise in one or more areas (Retail, CPG, Logistics, Manufacturing, Life Sciences, Finance and Insurance), strong business and technical acumen along with excellent soft skills.
What do we require?
To work with clients to understand the issues they face, diagnose problems, design solutions and facilitate solution deployment on the cloud. You can be an individual contributor or lead small teams depending on the project. You will be pivotal to understanding the requirement, problem definition and discovery of the overall solution. You will also have the opportunity to shape value-adding consulting solutions for clients by connecting various functions of cloud components.
Industry knowledge - Knows advanced concepts of machine learning, has deployed ML/AI solutions in the cloud, has a deep understanding of coding practices, knows how to guide teams on debugging issues, can connect the dots to arrive at a solution and is very good at presentation of the ideas, thoughts and solutions.

Technical knowledge- has expertise in building and deploying AI solutions in the cloud with hands on coding experience in –

• Python Programming - Expert and Experienced
• DevOps Working knowledge with implementation experience - 1 or 2 projects a minimum
• Hands-On MS Azure/AWS/GCP Cloud knowledge
• Experience in development of Information extraction pipelines (OCR, NLP, text/layout segmentation) using Open-source tools/Models (ex. tesseract, PaddleOCR, etc.) or Deep Neural Network (Yolo, Detectron, etc)
• Experience in fine tuning of pretrained models (pytorch or tensorflow)
• Experience in Data Modeling and Semantic search using sparse and dense indexes. Basic Understanding of LLM indexing tools and libraries (Langchain, LLamaindex, Haystack)
• Experience in Microservices development, API backend development using FastAPI
• MLOps (model/component dockerization, Kubernetes deployment) in multiple environments (AWS, AZURE, GCP). Operationalization of AI solutions to production.
• Relational DB (SQL), Graph DB (Neo4j) and Vector DB (Pinecone, Weviate, Qdrant)
• Guide team to debug issues with pipeline failures
• Engage with Business / Stakeholders with status update on progress of development and issue fix
• Automation, Technology and Process Improvement for the deployed projects
• Setup Standards related to Coding, Pipelines and Documentation
• Adhere to KPI / SLA for Pipeline Run, Execution
• Research on new topics, services and enhancements in Cloud Technologies

Responsible for successful delivery of Gen AI and Machine Learning solutions and services in client consulting environments;
Define key business problems to be solved; formulate high level solution approaches and identify data to solve those problems, develop, analyze/draw conclusions and present to client.
Assist clients with operationalization metrics to track performance of AI Models
Agile trained to manage team effort and track through JIRA
High Impact Communication- Assesses the target audience need, prepares and practices a logical flow, answers audience questions appropriately and sticks to timeline.

Education and Experience:

• Master’s degree in Computer Science Engineering or other related fields, with relevant experience in the field of MLOps / AI / Cloud
• Domain experience in Retail, CPG, Logistics, Manufacturing, Life Sciences, Finance or Insurance
• Exposure to US or European overseas markets is preferred

Domain / Technical / Tools Knowledge:

• Object oriented programming, coding standards, architecture & design patterns, Config management, Package Management, Logging, documentation
• Proficiency with data analysis tools (e.g., SQL, R & Python), database concepts/reporting & Data Science concepts
• Information extraction pipelines (OCR, NLP, text/layout segmentation) using Open-source tools/Models (ex. tesseract, PaddleOCR, etc.) or Deep Neural Network (Yolo, Detectron, etc)
• Pretrained models (pytorch or tensorflow)
• Data Modeling and Semantic search using sparse and dense indexes. LLM indexing tools and libraries (Langchain, LLamaindex, Haystack)
• Microservices development, API backend development using FastAPI
• MLOps (model/component dockerization, Kubernetes deployment) in multiple environments (AWS, AZURE, GCP). Operationalization of AI solutions to production.
• Relational DB (SQL), Graph DB (Neo4j) and Vector DB (Pinecone, Weviate, Qdrant)
• Experience designing and implementing ML Systems & pipelines, MLOps practices
• Exposure to event driven orchestration, Online Model deployment
• Hands on experience in working with client IT/Business teams in gathering business requirement and converting into requirement for development team
• Experience in managing client relationship and developing business cases for opportunities

All aspects of employment at Infosys are based on merit, competence and performance. We are committed to embracing diversity and creating an inclusive environment for all employees. Infosys is proud to be an equal opportunity employe

Please refer the Job description for details