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Fincons Group is looking for an experienced AI / Machine Learning Engineer to support data-driven initiatives across complex business and operational environments. This role focuses on identifying high-impact AI/ML use cases, developing and deploying machine learning solutions, and working closely with cross-functional stakeholders to turn data into actionable insights. The ideal candidate brings a strong technical foundation, a problem-solving mindset, and the ability to communicate effectively with both technical and non-technical teams.
Identify AI/ML Opportunities: Partner with cross-functional business and technical teams to identify and define AI and machine learning use cases that improve efficiency, performance, and decision-making. Help shape project scope, objectives, and success metrics.
End-to-End ML Delivery: Own machine learning projects throughout their lifecycle, including data collection, preprocessing, model development, deployment, and ongoing monitoring in production environments.
Programming & Data Engineering: Develop clean, maintainable code (primarily in Python) to build data pipelines, implement ML models, and automate analytical workflows. Use SQL for data extraction and transformation.
Data Analysis & Modeling: Analyze large, complex datasets from operational systems (e.g., ERP, MES, IoT, or similar sources) to identify trends, engineer features, and build predictive or prescriptive models.
Stakeholder Communication: Translate technical findings into clear, actionable insights for non-technical stakeholders to support data-driven decisions.
Qualifications & Experience
Experience: 3+ years of hands-on experience in AI, Machine Learning, or Data Science. Experience in industrial, manufacturing, or operational environments is a plus.
Technical Skills:
Strong proficiency in Python and common ML libraries (e.g., scikit-learn, TensorFlow, PyTorch)
Solid SQL skills for querying and data manipulation
Experience with data visualization tools (e.g., Power BI, Tableau, or similar)
Basic understanding of MLOps concepts, including model deployment and monitoring
Problem-Solving: Demonstrated ability to break down complex problems and deliver practical, data-driven solutions.
Communication & Collaboration: Strong communication skills with the ability to work effectively across technical and non-technical teams.
Nice to Have
Experience with cloud platforms such as AWS, Azure, or GCP
Exposure to time-series analysis, anomaly detection, optimization techniques, or similar advanced analytics
Familiarity with operational or manufacturing data and processes (e.g., quality, maintenance, supply chain)
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