Advanced AI Leader

Lead AI and GenAI Specialist

Incedo is a key level role in the Solutions group focused on evolving AI and GenAI driven problem discovery, root cause analysis and action recommendation modules to solve critical client problems at scale.

• Advanced AI and GenAI for Problem Discovery, RCA and Action Recommendation: Lead a team of data scientists, GenAI developers and ML engineers in hands-on mode. The team develops and deploys advanced LLM, SLM and RAG based models along with classical machine learning models including anomaly detection, predictive analytics, causal inferencing and action recommendation models, to develop pipelines that autonomously discover problems, identify root causes and recommend actions in real-time and batch processing scenarios while ensuring that models are interpretable, scalable, and optimized for performance in production environments at big data scale.
• Gen-AI-Powered Analysis and Insights Generation: Lead data scientists in hands-on mode creating and fine-tuning Generative AI models to assist in analysis creation for problem discovery, translating complex data patterns and root cause findings into actionable, natural language insights for business stakeholders. Implement prompt engineering and fine-tuning techniques to enhance the relevance and accuracy of insights.
• Reference Architectures: Work with Technical Architects to evolve frameworks for augmenting and evolving batch processing data architectures with streaming architectures to support connected data and ML pipeline executions in real-time. Adapt reference architectures that operationalize AI and Gen-AI models optimally in both batch and real-time modes.
• Client Deployments of the Platform: Consult and provide guidance for creating automated data pipelines for raw data and engineered features ensuring data quality, integrity, and accessibility for model training and inference. Help establish robust MLOps and LLMOps practices, including automated model monitoring, retraining triggers, and performance benchmarking including assessment of model accuracy and drift parameters and the business impact. Guide implementation of improvements based on feedback and new data.
• Development of Use Cases: Lead and support development of use cases on the platform for various vertical specific problem statements. Support platform demonstration meetings by participating and leading discussions around explanation of platform capabilities and fitments.
• Leadership and Collaboration: Lead and mentor a team of data scientists and machine learning engineers, providing guidance on best practices in GenAI, AI/ML methodologies and coding standards. Collaborate closely with product managers, data engineers, and business analysts to align AI solutions with platform goals and user requirements in accordance with the platform roadmap.
• Pre-sales: Support pre-sales, marketing hyper-scaler partnerships and other sales activities such as RFPs by providing subject matter expertise during scoping and designing of the engagements. Support the client development efforts by contributing to developing proposals, POVs and other POC read-outs. Support pilot deployments at clients and strive for conversion into full programs.

Required Skills and Qualifications

• Experience with AI and GenAI concepts, classical machine learning and deep learning methodologies and significant hands-on development experience in these areas.
• Ability to understand nuances of business problems in various domains, grasp problem discovery analysis and modeling imperatives and translate those into AI model requirements.
• Significant exposure to cloud-based Machine Learning platforms, open-source LLMs, prompt engineering, RAG based training and SLM model development.
• Hands-on experience with excellent ML programming skills in Python using open-source libraries.
• Experience working with RDBMS and NoSQL databases, exposure to Big Data technologies.
• 12-18 years of Machine Learning model development and application experience.
• Experience building cloud-based products implementing GenAI and ML modeling as a service.