Technology Lead

Infosys Public Services

Location: USA
Workplace: Remote
Employment: Full Time
Salary: USD 73,000–102,200/yr

Posted 1mo ago

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Responsibilities

Build and deploy Python applications or scripts that address IT operational needs, automate processes, or handle data management
Acquire, clean, preprocess, and transform data using Python tools and techniques for building robust analysis on large scale data sets
Implement and deploy Cloud Applications
Deploy Python applications in cloud service production environments (e.g., AWS, Azure, GCP), potentially leveraging containerization tools (e.g., Docker, Kubernetes)
Apply principles like version control (Git), writing clear and testable code, participating in code reviews, and using CI/CD pipelines
Implement data science solutions such as data pipelining, feature engineering, or creation of Machine Learning Models
Use managed AI/ML services provided by cloud platforms to streamline development, deployment, and management of data science applications
Apply ethical guidelines for data science solutioning, including addressing bias, ensuring data privacy, and implementing secure coding practices in Python-based solutions
Build MCP servers and integrate with Agentic AI workflows
Communicate complex technical concepts to both technical and executive stakeholders
Create technical diagrams with products like Microsoft Visio or Draw.io
Create technical design and architecture documents in Microsoft Word
Create business and technical presentations in Microsoft PowerPoint
Create data representations, charts and reports in tools such as Microsoft’s Excel worksheets and Power BI
Communicate, orally and in writing, sufficient to develop and present management briefings; provide written and/or verbal guidance on technical issues; and prepare/present recommendations and reports
Use design patterns for building scalable and maintainable applications/solutions
Document code, models, and technical solutions
Develop and demonstrate proof-of-concepts (PoC); independently or in a team
Create technical diagrams and documentation to show PoC implementations and potential production implementations
Research and present to teammates on the latest tools/packages/capabilities being developed
Make recommendations on relevant tools/packages to use for production environments
Work with relevant governance committees to document and obtain approval for exploratory data science efforts
Consult with members of architecture teams to identify potential automation solutions which may include AI/ML
Collaborate with cross functional teams on holistic AI/ML solutions
Query and manage data in both SQL and NoSQL databases
Perform data science tasks such as data acquisition, data cleaning, and feature extraction
Develop and demonstrate proof-of-concepts (PoC)
Create technical diagrams and documentation
Research and present on latest tools/packages/capabilities
Make recommendations on relevant tools/packages
Work with governance committees to document and obtain approval for exploratory data science efforts
Consult with architecture teams to identify potential automation solutions
Collaborate with cross functional teams on holistic AI/ML solutions
API development and integration

Requirements

Demonstrating a track record of building and deploying Python applications or scripts that address IT operational needs, automate processes, or handle data management
Demonstrable experience with data acquisition, cleaning, preprocessing, and transformation using Python tools and techniques for building robust analysis on large scale data sets
Hands-on experience with Databricks for large-scale data engineering, analytics, machine learning, and cloud-based data processing solutions
Experience deploying python applications in cloud service production environments (e.g., AWS, Azure, GCP), potentially leveraging containerization tools (e.g., Docker, Kubernetes)
Experience in applying principles like version control (Git), writing clear and testable code, participating in code reviews, and using continuous integration/continuous deployment (CI/CD) pipelines
Demonstrated understanding and implementation of data science solutions such as data pipelining, feature engineering, or creation of Machine Learning Models
Proficiency using managed AI/ML services provided by cloud platforms to streamline development, deployment, and management of data science applications
Demonstrated awareness and application of ethical guidelines for data science solutioning, including addressing bias, ensuring data privacy, and implementing secure coding practices in Python-based solutions
A bachelor’s degree or foreign equivalent is required from an accredited institution
Three years of progressive, relevant work experience instead of every year of education
Candidates who have completed a Master's degree program are strongly preferred
The job entails sitting and working at a computer for extended periods
Should be able to communicate by telephone, email, or face-to-face
Travel may be required as per the job requirements

Preferred

Hands on experience building MCP servers and integration with Agentic AI workflows
Communicating complex technical concepts to both technical and executive stakeholders
Proficiency creating technical diagrams with products like Microsoft Visio or Draw.io
Proficiency creating technical design and architecture documents in Microsoft Word
Proficiency creating business and technical presentations in Microsoft PowerPoint
Proficiency creating data representations, charts and reports in tools such as Microsoft’s Excel worksheets and Power BI
Ability to communicate, orally and in writing, sufficient to develop and present management briefings; provide written and/or verbal guidance on technical issues; and prepare/present recommendations and reports
Using design patterns for building scalable and maintainable applications/solutions
Clearly document code, models, and technical solutions
Proficiency in Generative AI and prompt engineering
Continuous learning and adaptability in a very large IT organization
Troubleshooting software and technical implementations in large-scale enterprise ecosystems
API development and integration
Querying and managing data in both SQL and NoSQL databases
Data science tasks such as data acquisition, data cleaning, and feature extraction
Develop and demonstrate proof-of-concepts (PoC); independently or in a team
Create technical diagrams and documentation to show PoC implementations and potential production implementations
Researching and presenting to teammates on the latest tools/packages/capabilities being developed
Make recommendations on relevant tools/packages to use for production environments
Work with relevant governance committees to document and obtain approval for exploratory data science efforts
Consulting with members of architecture teams to identify potential automation solutions which may include AI/ML
Collaborating with cross functional teams on holistic AI/ML solutions

Skills

Python
Artificial Intelligence
Data Science
Machine Learning
Databricks
AWS
Azure
GCP
Docker
Kubernetes
Git
CI/CD
Generative AI
SQL
NoSQL
Power BI
Microsoft Visio
Draw.io
Microsoft Word
Microsoft PowerPoint
Microsoft Excel

Responsibilities

Requirements

Preferred

Skills

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