Willware Technologies
Website:
willwaretech.com
Job details:
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? We’re 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
- Architect Agentic AI workflows using 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 reviews to 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 talent, identifying 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:
- Bachelor’s/Master’s/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.
Click on Apply to know more.