Senior Lead Engineer - AI
Nagpur · Staff/Principal
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What we know about this role
LOW
Konvergeai is showing limited hiring activity lately. Expect slight delayed response.
First 72 hours
Window passed - posted 60d agoEarly applicants get seen before the pile builds.
Not a repost
The first time we've seen this listing - it hasn't been closed and reopened.
Skills required
6 listedYou almost certainly match several of these already. Unlock your skill map to see the matches, the gaps, and what to fix first.
Job description
- Demonstrates proficiency in at least one of the five areas: Artificial Intelligence, Generative Ai, Natural Language Processing, Machine Learning, and Forecasting. Expected to deepen this expertise further.
- Translates data-related client requirements into project tasks and deliverables, with an understanding of complex problems and solutions.
- Drives data solutions within projects, with a focus on innovation and value creation.
- Provides technical guidance and support to team members on data science methodologies, tools, and algorithms.
- Acts as a point of escalation for data science-related issues within the project, working towards efficient resolution.
- Reviews and ensures adherence to performance parameters for data science tasks, with a focus on data quality and model performance.
- Balances data science workload within the team, considering the skills and capacities of team members.
- Champions learning initiatives for upskilling and cross-skilling in data science within the team, including knowledge sharing sessions and promoting best practices.
- Contributes to the development of new data science solution offerings, leveraging insights from project work.
- Encourages team members to build technical expertise and innovate within the realm of data science.
- Begins to build and nurture client relationships with a focus on identifying opportunities to add value through data science.
- Starts to develop proficiency in at least one of data engineering, MLOps, product engineering, and cloud services, and applies this knowledge within projects.
- Begins to contribute to the organization's thought leadership in data science, such as writing articles, giving presentations, or contributing to internal forums.
- Actively identifies and communicates opportunities for the organization to enhance its data science capabilities, including new tools, methodologies, or service offerings.
- Keeps an eye on industry trends in data science to ensure personal growth and the competitive advantage of the organization
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