AI Engineer

gevernova

Chennai, Tamil Nadu, India 2 Years Exp Posted 18d ago

Job Description

AI & ML Solution Development & Integration

  • Design, develop, and implement AI solutions, including generative AI, machine learning models, neural networks, and optimization algorithms, to improve business process efficiency and effectiveness.
  • Select the appropriate modelling approach for each problem and articulate the trade-offs behind that choice.
  • Own the machine learning lifecycle: dataset construction, metrics definition, acceptance criteria in accordance with the stakeholders needs, evaluation strategies.
  • Translate business and operational needs into scalable AI-enabled tools, applications, and workflows.
  • Support the deployment and integration of AI/ML models into existing business and technical systems, software environments, and products where applicable, including monitoring for drift, performance degradation, and running cost.

 

Technical Implementation & Architecture

  • Collaborate with domain experts to identify high-value use cases and define technical requirements for AI solutions.
  • Define and document the solution architecture end to end: from data sources to the integration with the existing enterprise and technical IT landscape.
  • Design cloud-ready and on-premises solutions aligned with company IT, cybersecurity and data-governance standards.
  • Integrate AI/ML capabilities into hardware, software, and business process ecosystems in a way that supports reliability, usability, and maintainability.
  • Contribute to the development of robust, production-ready AI solutions suitable for industrial environments.

 

Solution Delivery, Partner & Contractor Management

  • Write clear technical specifications, statements of work, and acceptance criteria for work delivered by external contractors, software vendors, or internal digital teams.
  • Contribute to supplier, platform, and tool selection through structured technical evaluation, benchmarking, and proof-of-concept comparison.
  • Steer and review the work of internal & external partners: technical follow-up, design reviews, code and model reviews, quality gates, and acceptance testing; remaining the technical owner and guardian of the delivered solution.
  • Ensure solutions remain maintainable after handover through documentation, knowledge transfer, and clearly assigned ownership, so that delivered tools do not become orphaned.

 

Data Strategy & Analytics

  • Lead or support the collection, processing, structuring, and analysis of large-scale operational and business data.
  • Assess data readiness ahead of any development (availability, quality, labelling needs, access rights, confidentiality) and define strategies to close the gaps.
  • Identify patterns, trends, and performance improvement opportunities using advanced analytics and AI methods.
  • Develop data-driven solutions such as predictive maintenance, anomaly detection, quality and performance prediction, forecasting, cost analysis,  document and requirement analysis, and knowledge support tools.

 

Cross-Functional Collaboration

  • Work closely with technical, operational, business and IT teams to ensure AI solutions meet business and industry requirements for safety, reliability, performance, and scalability.
  • Communicate technical concepts clearly to both technical and non-technical stakeholders.
  • Help align AI initiatives with business priorities, operational goals, and constraints.
  • Support end-user adoption: training, onboarding, feedback loops and measurement of the benefits realized once the solution is live.

 

Continuous Innovation

  • Evaluate emerging technologies such as edge AI, synthetic data, reinforcement learning, and large language models for industrial and business applicability.
  • Stay current with developments in AI, machine learning, and digital tools, and recommend practical adoption opportunities.
  • Maintain an active technology watch on the AI tooling landscape and filter it, distinguishing capability gains from hype before proposing adoption.
    • Contribute to building an innovation-oriented culture through knowledge sharing, experimentation, and continuous improvement.

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