As an AI engineer, you will facilitate the transfer of advanced AI technologies from the research labs to the domain testbeds and thus the real world. You will participate in the full research to deployment pipeline. You will help conceptualize and develop research experiments, and then implement the systems to execution these experiments. You will lead or work with a team and interact closely with deep experience machine learning engineering and research and the industry partners. You will attend reading groups and seminars, master research techniques and engineering practices, and design research tools and experimental testbeds. You will apply state-of-the-art AI algorithms, explore new solutions, and build working prototypes. You will also learn to deploy the systems and solutions at scale.
- Use Deep Learning and Machine Learning to create scalable solutions for business problems.
- Deliver Deep Learning/Machine Learning projects from beginning to end, including business understanding, data aggregation, data exploration, model building, validation and deployment.
- Define Architecture Reference Assets - Apply Accenture methodology, Accenture reusable assets, and previous work experience to delivery consistently high quality work. Deliver written or oral status reports regularly. Stay educated on new and emerging market offerings that may be of interest to our clients. Adapt to existing methods and procedures to create possible alternative solutions to moderately complex problems
- Work hands on to demonstrate and prototype integrations in customer environments. Primary upward interaction is with direct supervisor. May interact with peers and/or management levels at a client and/or within Accenture.
- Solution and Proposal Alignment - Through a formal sales process, work with the Sales team to identify and qualify opportunities. Conduct full technical discovery, identifying pain points, business and technical requirements, “as is” and “to be” scenarios.
- Understand the strategic direction set by senior management as it relates to team goals. Use considerable judgment to define solution and seeks guidance on complex problems.
- Basic qualifications
- Bachelors degree in AI, Computer Science, Engineering, Statistics, Physics.
- Minimum of 1 year of experience in production deployed solutions using artificial intelligence or machine learning techniques.
- Minimum of 1 years previous consulting or client service delivery experience
- Minimum of 2 years of experience with system integration architectures, private and public cloud architectures, pros/cons, transformation experience
- Minimum of 1 year of full lifecycle deployment experience
- Masters or PhD in Analytics, Statistic or other quantitative disciplines
- Deep learning architectures: convolutional, recurrent, autoencoders, GAN’s, ResNets
- Experience in Cognitive tools like Microsoft Bot Framework & Cognitive Services, IBM Watson, Amazon AI services
- Deep understanding of Data structures and Algorithms
- Deep experience in Python, C# (.NET), Scala
- Deep knowledge with MxNet, CNTK, R, H20, TensorFlow, PyTorch
- Highly desirable to have experience in: cuDNN, NumPY, SciPy