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Principal Engineer – AI and Data Science
Ge Vernova
Providing technical leadership and developing next generation data fabric system for AI-Based Application in Grid Software business.
16d ago
220,000
Expert & Leadership (13+ years)
Full Time
Hyderabad, Telangana, India
Office Full-Time
Company Size
76,800 Employees
Service Specialisms
Energy Services
Digital Solutions
Sustainability
Smart Grid Technologies
Consulting
Sector Specialisms
Power
Gas Power
Steam Power
Nuclear
Hydro Power
Wind
Onshore Wind
Offshore Wind
Role
What you would be doing
developing ai technologies
data fabric system
technical leadership
strategic planning
influence strategy
decision impact
- Finds important patterns in seemingly unrelated information.
- Balances value propositions for competing stakeholders. Recommends a well-researched recommendation of buy vs. build solution.
- Capture system level requirements by brainstorming with CTO, Sr. Architects, Data Scientists, Businesses & Product Managers
- Work on developing next generation AI technologies such as Text Mining, Machine Learning, Deep Learning to improve product capabilities and development processes.
- Work with Product Line Leaders (PLLs) to understand product requirements & develop a strategy and vision.
- Be responsible for providing technical leadership and defining, developing, and building our next generation data fabric system with focus on semantic modeling, data catalog development, data access and integration layers software in a fast paced and agile development environment.
- Influences through others; builds direct and "behind the scenes" support for ideas.
- Conveys the value proposition for the company by assessing financial risks and gains of decisions and return on investment (ROI).
- Evaluates technology to drive features and roadmaps. Maps technology trends to internal vision.
- Proactively identifies and removes project obstacles or barriers on behalf of the team.
- Identifies how the cost of change weighs against the benefits and advises accordingly.
- Drive innovation in our database systems and drive next level of performance, correctness, durability, and scalability of our data systems by bringing in AI and next generation modeling technologies.
- Provide guidance to developers with either planning and execution and/or design architecture using agile methodologies such as SCRUM.
- Facilitates and coaches software engineering team sessions on requirements estimation and alternative approaches to team sizing and estimation. Leads a community of practice around estimation to share best practices among teams.
- Pre-emptively sees downstream consequences and effectively tailors influencing strategy to support a positive outcome. Uses experts or other third parties to influence.
- Maintains excitement for a process and drives to new directions of meeting the goal even when odds and setbacks render one path impassable.
- Leads impact assessment and decision related to technology choices, design /architectural considerations, and implementation strategy.
What you bring
ai
data systems
design
distributed systems
master's degree
problem-solving
- Subject matter expert in AI, data quality, data systems and related technologies with ability to adapt and improvise in various situations. Expert in navigating through ambiguity and prioritizing conflicting asks.
- Deep knowledge in AI, data quality, data systems and knowledge in methods such as B+ trees, LSM trees, Lamport clocks, etc.
- Is open to embracing a new language/stack/polyglot
- Innovates and integrates new processes and/or technology to significantly add value to GE.
- Deep knowledge in distributed systems with focus on key database concepts like concurrency control, consensus algorithms, indexing, replication, and serialization.
- Proactively learns new solutions and processes to address seemingly unanswerable problems.
- Understands when change is needed. Participates in technical strategy planning.
- Expert level skills in design, architecture, and development, with an ability to take a deep dive in the implementation aspects if the situation demands.
- Enjoy working remotely as part of a fully distributed team
- Working on data system across multiple operating systems/cloud environments is considered strong plus.
- Must have experience building systems or components of AI system including semantic modeling, distributed data management software
- Have deep mastery of at least one of: C#, C/C++, Java, Kotlin, Rust, Scala, or other systems/primary enterprise language that is used in data systems
- Able to navigate accountability in a matrixed organization.
- Proactively engages with cross-functional teams to resolve issues and design solutions using critical thinking and analysis skills and best practices.
- Have experience building cross-platform distributed data management software
- Able to clearly articulate the business drivers relevant to a given initiative.
- Experience in working in industrial environments is a strong plus.
- Manages the process of building and maintaining a successful alliance. Understands and successfully applies common analytical techniques, including ROI, SWOT, and Gap analyses.
- Communicates and demonstrates a shared sense of purpose. Learns from failure.
- Expert in core areas of semantic modeling, data structures, knowledge graph technologies and can implement them using language of choice when necessary – as a value offering.
- Able to verbalize what is behind decisions and downstream implications. Continuously reflecting on success and failures to improve performance and decision-making.
- Differentiates buzzwords from value proposition. Embraces technology trends that drive excellence beyond traditional practices (e.g., Test automation in lieu of traditional QA practices).
- Able to effectively direct and mentor others in critical thinking skills.
- Master’s Degree in Computer Science or “STEM” Majors (Science, Technology, Engineering and Math) with minimum 18 years of experience.
- Advanced degrees such as PhD specially in AI, distributed computing or data systems is strong plus.
Information not given or found
Training + DevelopmentInformation not given or found
Interview process
Information not given or found
Visa SponsorshipInformation not given or found
Security clearance- background checks may be required for the job
Company
Overview
April 2024
Founded
The company emerged from the spin-off of GE's energy units in April 2024.
>$10B Quarterly Revenue
Revenue Growth
Achieves over $10 billion in quarterly revenue, driven by demand for power infrastructure and digital solutions.
$3B
Wind Turbine Backlog
Maintains a significant backlog in wind turbine orders, reflecting strong market demand.
25%
Global Electricity Supply
Contributes to generating 25% of the world’s electricity through its installed turbines and grids.
- Traces roots back to Edison and Alstom, merging power, renewable, digital & financial wings.
- Headquartered in Cambridge, MA, crafts large-scale gas turbines, SMRs, wind turbines, hydro and grid tech to fuel economies.
- On the nuclear front, advancing small modular reactors (like BWRX‑300) in partnership with utilities and supporting semiconductor projects.
- Wind prowess spans onshore, offshore and blade making—with key sites like Dogger Bank offshore and blade plants in Spain.
- Electrification arm tackles grid stability: HVDC, transformers, storage, conversion, plus GridOS software powering smarter infrastructure.
- Weaves finance and consulting through energy-infrastructure investments, funding solar farms to pipelines via GE Energy Financial Services.
- Relentlessly focused on advancing the world’s transition to cleaner, more sustainable energy.
- Believes in working with customers, partners, and communities to create innovative energy solutions that make a meaningful difference.
- Prioritizes excellence, integrity, and accountability in everything they do.
- Committed to driving real change through technology and partnerships that will transform the global energy landscape.
2050 target
Net zero commitment
Committed to achieving net zero carbon emissions by 2050.
- Focused on reducing emissions through advanced energy technologies.
- Maximizing use of renewable energy sources and leveraging digital solutions for energy efficiency.
- Solutions aim to decarbonize industries and help customers meet their sustainability goals.
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