Project Role :
Large Language Model Architect
Project Role Description :
Architect large language models (LLM) that can process and generate natural language. Design neural network parameters, trained on large quantities of unlabeled text data.
Must have skills :
Large Language Models
Good to have skills :
NA
Minimum 12 Year(s) Of Experience Is Required
Educational Qualification :
15 years full time educationSUMMARY: We are seeking a Senior Technical – GenAI Lead with deep expertise in Python, AI/ML concepts, and data engineering to spearhead the design and development of Data Agents powered by Agentic AI. As a senior technical leader, you will drive solution architecture, guide teams, and ensure the successful delivery of AI-enabled initiatives that align with business goals. The ideal AI Lead combines technical excellence, leadership ability, prompt engineering skills, and a strong product mindset to ensure solutions are impactful, scalable, and business-driven. ROLES AND RESPONSIBILITIES: 1. Solution Architecture & Delivery
- Lead the design, architecture, and implementation of AI-enabled solutions powered by Agentic AI.
- Ensure scalability, reliability, and alignment of solutions with business objectives. 2. Team Leadership & Guidance
- Mentor and guide cross-functional teams in AI/ML, data engineering, and product development.
- Foster a collaborative environment that drives innovation and high-quality delivery. 3. AI/ML & Data Engineering Expertise
- Spearhead the development of intelligent Data Agents leveraging Python, AI/ML models, and scalable data pipelines.
- Apply advanced prompt engineering techniques to optimize the performance of LLMs and agentic workflows. 4. Collaboration & Stakeholder Engagement
- Work closely with product managers, data scientists, and engineering teams to define AI-driven use cases.
- Translate business goals into technical requirements and actionable AI solutions. 5. Innovation & Emerging Technologies
- Stay ahead of trends in GenAI, Agentic AI, ML, and autonomous systems.
- Introduce best practices, frameworks, and tools to enhance solution effectiveness and maintain competitive advantage. 6. Business-Driven Mindset
- Align AI solutions with organizational strategy to maximize impact and business value.
- Ensure delivered solutions are scalable, secure, and capable of driving measurable business outcomes. TECHNICAL EXPERIENCE:
- Min 10 years of professional experience in the required skills - having min 2 year of GenAI experience
- Lead the design and development of Data Agents leveraging Agentic AI principles, Python, and AI/ML frameworks.
- Strong expertise in Python and PySpark for large-scale data engineering.
- Solid foundation in AI/ML concepts (e.g., supervised/unsupervised learning, reinforcement learning, LLMs).
- Experience architecting and deploying AI-driven solutions in production environments.
- Proven leadership skills in managing teams, mentoring talent, and leading technical delivery.
- Strong prompt engineering skills to refine LLM interactions and improve system outcomes.
- Excellent communication and stakeholder management skills, with the ability to bridge business and technology.
- Product mindset with a focus on usability, scalability, and delivering business impact.
- Growth mindset and adaptability to thrive in an evolving digital and AI landscape
- Define and own the technical architecture for scalable, secure, and efficient agent driven systems.
- Apply prompt engineering techniques to optimize LLM performance and enhance agent effectiveness.
- Guide and mentor analysts and developers in Python, PySpark, AI/ML best practices, and agentic system design.
- Partner with product managers, data scientists, and business stakeholders to translate requirements into AI-driven solutions.
- Drive a product-oriented approach, ensuring AI agents deliver measurable value aligned with business outcomes.
- Ensure code quality, performance optimization, and adherence to engineering best practices.
- Stay ahead of emerging trends in Agentic AI, LLMs, and autonomous systems to bring innovation into practice.
- Lead proofs-of-concept (POCs) and enterprise-scale implementations of AI-based automation. ADDITIONAL ATTRIBUTES:
- Hands-on experience with LLM frameworks (e.g., LangChain, LlamaIndex) and Agentic AI orchestration tools.
- Familiarity with cloud AI/ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI).
- Exposure to data governance, security, and compliance practices in enterprise AI systems.
- Experience with visualization and reporting tools to support data-driven decision making. EDUCATION QUALIFICATIONS:
- A 15-year full-time education is required., 15 years full time education
