Technology Services - Senior Analyst - Machine Learning Consultant Job in Eclerx

Description

We are looking to add to this talent pool someone who can provide quality answers to large-scale problems with broad impact.

The Ideal Experience Map:

Graduate with 2-4 years of relevant experience with an expert-level understanding of Artificial Intelligence technologies like knowledge representation and reasoning (KRR), natural language processing (NLP), speech recognition, unsupervised machine learning, and/or reinforcement learning.

Roles and Responsibilities:

• Responsible for machine learning technology and application analysis, understand latest industrial and academic developments in AI / ML
• Study and innovate in deep learning / machine learning and its application in diverse domains
• Design competitive AI / ML services and user experience and create prototypes for demonstration
• Work with the rest of the Operations team to integrate these algorithms into larger solutions
• Work with development teams to mature these algorithms

People Skills and Personality Traits:

• Competency in emerging and disruptive technologies and practices with a demonstrated capability in innovation
• Highly capable in thinking systematically, capable to redefine solutions to overcome existing competition
• Self-driven, energetic, creative, with ability to work in international teams
• Curious and willing to challenge existing solutions with innovative technology concepts

Technical Skills:

• Proven knowledge in deep learning algorithms and AI system design and architecture
• Excellent understanding of complex system architecture, components and requirements
• Advanced level proficiency in Python programming especially in conceptual understanding of relational databases and hands-on comfort working within a Linux computing environment, and use of numerical/analytical computing using Numpy, Scipy, and Pandas
• Relevant experience for the area of Knowledge Representation and Reasoning (KRR) and related information integration; propositional logic, first-order logic, and constraint satisfaction methods; methods for uncertainty in combination with logical methods (e.g., Bayesian, probabilistic soft logic); combination of the foregoing with natural language and combination of the foregoing with machine learning