Senior Artificial Intelligence Engineer

About us:

Finonyx

1. AI/ML Solution Design & Development

• Design, develop, and optimize advanced machine learning and deep learning models for production use.
  
• Select appropriate algorithms, model architectures, frameworks, and libraries based on business requirements.
  
• Implement scalable ML pipelines for data ingestion, preprocessing, model training, validation, and deployment.
  
• Build end-to-end AI solutions including NLP, computer vision, recommendation systems, and predictive analytics.

2. Data Engineering & Feature Development

• Work with large-scale structured and unstructured datasets.
  
• Perform feature engineering, data cleaning, and exploratory data analysis (EDA).
  
• Collaborate with data engineering teams to define data architecture, ETL flows, and real-time data streaming requirements.
  
• Ensure data quality, consistency, and reliability for model performance.

3. Model Deployment & MLOps

• Deploy models into production environments using CI/CD and MLOps practices.
  
• Design and manage model lifecycle: versioning, monitoring, retraining, and continuous improvement.
  
• Optimize model latency, throughput, and resource usage for high-performance production systems.
  
• Work with containerization and cloud platforms (AWS/GCP/Azure), Kubernetes, Docker, MLflow, etc.

4. Research & Innovation

• Explore, evaluate, and implement state-of-the-art AI/ML techniques and frameworks.
  
• Conduct POCs for innovative AI solutions and evaluate feasibility for production.
  
• Stay updated with advancements in AI, LLMs, deep learning trends, and emerging tools.
  
• Contribute to the creation of reusable components, accelerators, and AI frameworks.

5. Cross-Functional Collaboration

• Work closely with product owners, business analysts, architects, and software teams.
  
• Translate business problems into technical AI/ML solutions with measurable outcomes.
  
• Present model results, create documentation, and explain complex concepts to non-technical stakeholders.
  
• Participate in pre-sales, customer discussions, and solution architecture presentations (if applicable).

6. Performance Optimization & Quality Assurance

• Ensure models meet accuracy, performance, and reliability benchmarks.
  
• Conduct error analysis, hyperparameter tuning, and model refinement.
  
• Implement rigorous testing: A/B testing, model validation, bias detection, and scalability testing.
  
• Ensure adherence to AI ethics, data privacy, and compliance guidelines.

7. Mentoring & Leadership

• Guide junior AI/ML engineers, data scientists, and interns.
  
• Review code, ensure best practices in data science and ML engineering workflows.
  
• Lead technical design discussions, architecture reviews, and knowledge-sharing sessions.

8. Documentation & Reporting

• Document ML experiments, datasets, designs, architecture diagrams, and deployment guidelines.
  
• Maintain model cards, technical reports, and dataset documentation.
  
• Prepare executive-level reports for stakeholders and leadership.