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Our client, a leading global specialist in energy management and automation is looking to engage with a Consultant AI Algorithm Engineer to develop and optimize core machine learning and deep learning algorithms aimed at enhancing internal operational efficiency. You will work closely with business teams to identify automation opportunities, build AI models, and deploy scalable AI solutions that streamline workflows and support data-driven decision-making within the organization.
Key responsibilities:
| Define and Align AI Solutions with Internal Efficiency Goals - Collaborate with business stakeholders to identify process bottlenecks and areas for automation. - Translate business needs into technical problem statements and success metrics for AI projects. - Evaluate and propose the most suitable algorithmic approaches, balancing technical feasibility, business value, and long-term maintainability. - Present technical proposals and progress updates to non-technical stakeholders in a clear and actionable manner. |
| Design, Develop, and Optimize Core Machine Learning Models - Architect and implement supervised, unsupervised, and deep learning models for applications such as predictive analytics, anomaly detection, and natural language processing. - Build predictive models to forecast, develop anomaly detection systems to monitor and implement natural language processing models to automate document classification and information extraction. - Innovate and apply state-of-the-art techniques to continuously improve model accuracy, efficiency, and generalization ability. |
| Implement Robust Model Training and Validation Pipelines - Design and implement pipelines for data preprocessing, model training, evaluation, and versioning - Establish rigorous model validation, A/B testing, and monitoring frameworks to track performance, detect drift, and trigger retraining in production. - Document model assumptions, limitations, and deployment guidelines for internal users. |
| Deploy and Integrate Models into Internal Tools and Systems - Containerize models e.g. using Docker and deploy them as microservices in cloud environments. - Integrate models with existing internal platforms or collaboration tools. - Develop robust APIs and data interfaces to enable seamless interaction between models and business applications. - Optimize models and serving infrastructure for low-latency inference and cost-effectiveness in a production environment. - Provide technical support and troubleshooting for deployed models to ensure reliability. |
Champion Best Practices and Foster Technical Growth - Stay at the forefront of AI/ML research and evaluate emerging technologies for potential application to our business challenges. |
Duration- 12 months (extendable)
Location- Bangalore Avinya campus (Hybrid)
Capacity- Full time
1. Bachelor’s or higher degree in Computer Science, Artificial Intelligence, or a related field.
2. Expert-level proficiency in Python and deep, hands-on experience with frameworks (e.g. PyTorch, TensorFlow). Strong foundation in data tools (e.g. Pandas, NumPy, SQL).
3. Deep theoretical understanding and practical experience with a wide range of ML algorithms (e.g., CNN, XGBOOST, SVR, KNN) and their applications.
4. Proven track record of owning the end-to-end lifecycle of multiple ML projects that have been successfully deployed to production, delivering measurable business impact.
5. Excellent problem-solving, communication, and stakeholder management skills. Ability to lead initiatives, and work effectively in a collaborative, cross-functional environment.
6. At least 6 to 7 years of relevant experience.
Preferred Qualifications:
6. Experience in building and deploying model-serving APIs and integrating ML solutions into larger software ecosystems.
7. Solid experience in establishing MLOps practices and familiarity with cloud AI/ML services (e.g., AWS SageMaker, Google GCP Vertex AI, Azure ML, or Alibaba Cloud PAI).
A leading global specialist in energy management and automation
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