Design, build and deploy intelligent single / multi-agentic application that leverage GenAI models (e.g. LLMs) and perform task decomposition, planning and execution.
Develop and maintain end-to-end AI-powered workflows integrated with backend systems, APIs and data pipelines on cloud-based platforms (example Microsoft Azure, AWS).
Develop, refine, and implement advanced NLP (natural language processing) & predictive models to generate insights from complex structured and unstructured data sets.
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Practice & Delivery Leadership: Oversee the end-to-end delivery of multiple concurrent AI engagements, ensuring projects meet or exceed client expectations in impact, timeline, and quality. Act as Solution Architect and final technical authority, guiding design of complex AI systems (enterprise LLM applications, CV/NLP/analytics solutions, etc.) and ensuring robust, scalable architectures. Establish and enforce best practices, delivery methodologies, and governance standards across the AI engineering practice.
Sales & Business Development: Drive go-to-market and sales efforts for AI engineering services. Partner with account teams to identify and pursue new business opportunities, shape value propositions, and lead client proposals and pitches. Own the development of compelling use-cases, demos, and prototypes that showcase our AI capabilities (e.g., generative AI assistants, vector database-driven search solutions, industry-specific AI accelerators). Take accountability for achieving sales targets and expanding the project pipeline.
P&L and Practice Management: Serve as the P&L owner for the AI engineering practice or portfolio. Develop business plans and pricing strategies for engagements, managing resource allocation, utilization, and profitability. Monitor financial performance and implement measures to improve margins and delivery efficiency (e.g., optimizing reuse of assets, balancing onshore/offshore mix, efficient GPU resource management to control cloud costs).
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Practice & Delivery Leadership: Oversee the end-to-end delivery of multiple concurrent AI engagements, ensuring projects meet or exceed client expectations in impact, timeline, and quality. Act as Solution Architect and final technical authority, guiding design of complex AI systems (enterprise LLM applications, CV/NLP/analytics solutions, etc.) and ensuring robust, scalable architectures. Establish and enforce best practices, delivery methodologies, and governance standards across the AI engineering practice.
Sales & Business Development: Drive go-to-market and sales efforts for AI engineering services. Partner with account teams to identify and pursue new business opportunities, shape value propositions, and lead client proposals and pitches. Own the development of compelling use-cases, demos, and prototypes that showcase our AI capabilities (e.g., generative AI assistants, vector database-driven search solutions, industry-specific AI accelerators). Take accountability for achieving sales targets and expanding the project pipeline.
P&L and Practice Management: Serve as the P&L owner for the AI engineering practice or portfolio. Develop business plans and pricing strategies for engagements, managing resource allocation, utilization, and profitability. Monitor financial performance and implement measures to improve margins and delivery efficiency (e.g., optimizing reuse of assets, balancing onshore/offshore mix, efficient GPU resource management to control cloud costs).
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Design and Build AI Agents - Develop intelligent, multi-step AI agents capable of reasoning, planning, and decision-making using frameworks like LangChain, AutoGen, or CrewAI. Apply these to automate and accelerate tasks in security operations and penetration testing.
Implement Retrieval-Augmented Generation (RAG) - Work on the full RAG lifecycle—from designing chunking and hybrid retrieval strategies for domain-specific data to integrating agentic patterns for better context-aware reasoning.
Create Autonomous Workflows - Translate user needs into workflows that can think, plan, and act independently.
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Engage with clients to understand their challenges and recommend building, orchestrating, and running complex AI workflows and agents using large language models (LLMs)
Identify, analyze, and interpret trends or patterns in complex data sets to develop business solutions by determining the best I&I solutions.
Build strong relationships with clients and foster business growth with clear communication and stakeholder engagement.
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Contribute technically to analytics projects and deliver high-quality work products
Work effectively as a team member, sharing responsibility, providing support, maintaining communication, and updating senior team members on progress
Demonstrate in-depth technical capabilities and professional knowledge. Demonstrate ability to assimilate new knowledge. Possess good business acumen, remain current on new developments in advisory services capabilities.
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Contribute technically to analytics projects and deliver high-quality work products
Work effectively as a team member, sharing responsibility, providing support, maintaining communication, and updating senior team members on progress
Demonstrate in-depth technical capabilities and professional knowledge. Demonstrate ability to assimilate new knowledge. Possess good business acumen, remain current on new developments in advisory services capabilities.
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Lead the execution of client-facing data science, analytics, and data engineering projects from scoping through deployment and handover.
Design analytical approaches and solution architectures using our low-code data science studio, visual ETL platform, and enterprise lakehouse (with change-data-capture capabilities).
Write Python to extend, integrate, or bridge these platforms — custom transformations, model logic, APIs, automation, and anything the visual tools can't handle out of the box.
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