Director

Lead – AI and GenAI, Incedo is a key level role in the Solutions group at Incedo focused on Incedo Lighthouse TM platform that provides architectural and algorithmic leadership to evolve AI and GenAI driven problem discovery, root cause analysis and action recommendation modules within Incedo LighthouseTM to solve critical client problems at scale. This role works with the technical architects and data scientists in the team to create and evolve:

• Advanced AI and GenAI for Problem Discovery, RCA and Action Recommendation: Lead the team of data scientists, GenAI developers and ML engineers in hands-on mode. The team is engaged in developing and deploying 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 the 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 the 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.

Skills & Qualifications

• In-depth conceptual understanding of Statistics, Classical Machine Learning, Deep Learning and GenAI along with significant hands-on development experience in these areas
• Able to understand the nuances of business problems in various domains, quickly grasp the problem discovery analysis and modelling imperatives and translate those into the requirements for AI models
• Significant exposure to various proprietary & open-source cloud-based Machine Learning platforms such as Amazon Sagemaker / Azure Machine Learning Studio / Google Datalab – ML Engine – AutoML / H2O etc.
• Experience in leveraging open-source LLMs and prompt engineering, RAG based training and SLM model development
• Hands-on with excellent ML programming skills in Python using open-source libraries
• Experience in working with RDBMS and NoSQL databases; Exposure to Big Data technologies
• 12-18 years of Machine Learning model development and application experience
• Experience in building cloud-based products that implement GenAI and ML modelling as a service