Senior AIML Architect (Generative AI Focus)

Company: WillWare Technologies
Role:Senior AI/ML Architect (Generative AI Focus)
Location: Bangalore
Work Mode: Onsite

Job Description:
Are you passionate about building cutting-edge Generative AI solutions and leading high-impact AI initiatives Were looking for a Senior AI/ML Architect to design and implement scalable AI systems that transform business processes.

AI/ML Solution Architecture & Development:

• Design and implement end-to-end AI/ML solutions ensuring scalability performance and alignment with business objectives.
• Design and implement end-to-end RAG(Retrieval-Augmented Generation) systems with optimized knowledge retrieval
• ArchitectAgentic AI workflowsusing frameworks like LangChain LlamaIndex and AutoGen
• Develop multi-agent systems with specialized roles and orchestration capabilities
• Implement dynamic routing and decision-making logic for agent networks
• Architect and implement RAG (Retrieval-Augmented Generation)systems for knowledge-intensive applications
• Design and optimize document processing pipelines including advanced chunking strategies and metadata extraction
• Develop and fine-tune LLMs for domain-specific applications
• Architect and optimize AI pipelines including data ingestion preprocessing model training and deployment.
• Lead the integration of Generative AI (GenAI) technologies into existing workflows to enhance automation and decision-making.

Technical Leadership & Innovation:

• Provide technical guidance and mentorship to AI/ML teams fostering a culture of innovation and excellence.
• Stay abreast of the latest advancements in AI/ML and GenAI evaluating and adopting cutting-edge technologies as appropriate.
• Collaborate with cross-functional teams to align AI initiatives with business goals.

Code Quality & Engineering Best Practices:

• Enforce high standards of code quality maintainability and scalability in AI/ML implementations.
• Conduct and oversee code reviewsto ensure adherence to best practices and architectural guidelines.
• Implement robust testing frameworks including unit tests integration tests and system tests to validate AI models and pipelines.
• Lead the evaluation and selection of appropriate embedding models for specific use cases
• Design optimal chunking strategies for different document types (PDFs HTML plain text)
• Establish best practices for vector database implementation and optimization
• Mentor junior engineers in RAG implementation and document processing techniques

Evaluation Methods & Performance Metrics:

• Define and implement comprehensive evaluation methodologies to assess model performance ensuring all metrics met.
• Monitor and analyze model performance during pre-release testing and post-release phases to identify and address defects.
• Establish metrics and KPIs to measure the effectiveness and reliability of AI solutions in production.

Defect Management & Post-Release Monitoring:

• Develop strategies for defect prevention detection and resolution throughout the AI/ML development lifecycle.
• Lead root cause analysis for post-release defects and implement corrective actions to prevent recurrence.
• Work closely with DevOps and QA teams to ensure seamless deployment and monitoring of AI models in production.

Hiring & Team Development:

• Lead the recruitment process for AI/ML talentidentifying and hiring skilled professionals to build high-performing teams.
• Develop and implement strategies for team growth including training programs and career development plans.

Required Skills & Qualifications:

• Technical Expertise:
  • 5-10 years of experience in AI/ML with a strong focus on architecting and deploying AI solutions.
  • Deep knowledge of machine learning deep learning and NLP with proficiency in Python and frameworks like PyTorch TensorFlow or Hugging Face.
  • Experience with cloud platforms and MLOps practices for model deployment and monitoring.
• GenAI Experience:
  • Hands-on experience with LLMs RAG pipelines and AI agent frameworks.
  • Familiarity with vector databases (e.g. Melvis Pinecone Weaviate) and graph databases.
• Code Quality & Testing:
  • Strong understanding of software engineering principles including code quality testing and CI/CD pipelines.
  • Experience with testing frameworks and methodologies for AI/ML models.
• Evaluation & Defect Management:
  • Proven ability to define and implement evaluation metrics and performance monitoring systems.
  • Experience in defect management and root cause analysis for AI/ML systems.
• Leadership & Hiring:
  • Proven ability to lead technical teams and hire top AI/ML talent.
  • Strong communication and collaboration skills to work with stakeholders at all levels.
• Education:
  • Bachelors/Masters/PhD in Computer Science Data Science or a related field.

Preferred Qualifications:

• Experience in industries such as healthcare finance or manufacturing where AI/ML solutions have been successfully deployed.
• Publications or contributions to the AI/ML community such as research papers open-source projects or conference presentations.

Required Skills:

AIMLGenerative DesignMachine LearningNLP