Generative AI Practice Leader - ENU

The Generative AI Practice Leader is accountable for driving growth, strategic positioning and to ensure success in the future (e.g., practice focus areas, offerings, capabilities). Extensive experience in Advisory, Solutioning, Presales, Delivery and Global experience.

Location Houston, TX (HYBRID)

Expectations

• Responsible to formulate business development strategies on Generative AI for both customers and Client Gen AI practice.
• Experience in Oil & Gas domain would be a plus.
• Create differentiated solution & Services offerings and translate into revenue growth.
• Drive strategic account growth and be responsible for the delivery of consulting services to our customers.
• Actively search for opportunities to expand business within existing accounts and help win new clients.
• Building new technology capabilities, and building team competencies
• Marketing of technology & domain solutions / service offerings to internal/external stakeholders
• Manage business relationship with the technology partners & start-up eco systems and demonstrate edge over competition.
• Passionate about technology and customer success with excellent communication and articulation skills
• Ability to communicate technical concepts and solutions at a level appropriate for technical and non-technical audiences.
• Research, curate, and record tutorials on various applications of data science and machine learning
• Ability to work effectively in a dynamic, research-oriented group that has several concurrent projects.

Behavior Competencies

• Excellent Communication
• Consulting and Advisory
• Conflict Resolution
• Solutioning
• Customer Service
• Accountability
• Judgement and decision making

Technical Skills

• At least 5 years of hands-on experience with building language models, machine learning and AI models leveraging industry tools, products, and / or Azure cognitive services.
• Deep and hands-on expertise in (as an individual contributor) Undertaking data collection, Data mining, preprocessing and analysis of structured and unstructured data.
• Hands-on expertise in large language models (LLMs/LSTMs/BERT) that can perform complex reasoning in few- and zero-shot settings by generating intermediate chain of thought (CoT) reasoning steps.
• Experience of building / customizing and fine-tuning AI models including LLM models via OpenAI (Azure), Bert (AWS) for rapid PoCs
• Experience on LLM Model Governance, LLMSecOps, Hallucination and bias handling
• Deep and hands-on experience in applying machine learning algorithms.
• Strong data science and data engineering background both with open source and cloud distributed machines learning and AI tools especially Azure Cognitive Services, Azure Machine Learning and AWS Sagemaker and Bedrocks

• Strong understanding of Azure cloud technology including Cognitive Services viz. Vision, Speech, Language, Decision, Search and GenAI and its implementation

• Proven experience with, and applied knowledge of, at least one scripting language, such as Python, or R