Senior Lead Engineer

Lead the technical design and implementation of enterprise-scale data and AI solutions, ensuring alignment with business objectives and technology standards

• Review solution designs, code, and technical deliverables to ensure quality, performance, and adherence to architectural standards
• Mentor and guide development teams by resolving technical challenges and ensuring best practices
• Design and implement production-ready applications using LLMs (GPT-4, Claude, Gemini) and other foundation models.
• Build and optimize RAG (Retrieval-Augmented Generation) systems using vector databases like Pinecone, Weaviate, or Qdrant.
• Design and implement hybrid RAG systems, specifically utilizing LightRAG/GraphRAG (Knowledge Graphs) alongside vector databases to enable multi-hop reasoning across complex operational data.
• Implement model fine-tuning pipelines for domain-specific applications using techniques like LoRA and QLoRA.
• Create multi-agent systems and complex AI workflows using frameworks like LangGraph or AWS Strands, including Amazon Bedrock for foundation models.
• Integrate multiple AI models (text, vision, audio) to create multimodal applications and work with protocols like MCP and A2A to extend the capabilities of the system.
• Architect secure, event-driven integrations between the AI platform and Enterprise ITSM tools (e.g., Jira, MS Teams, GitLab) using webhooks and message brokers.
• Implement guardrails and safety measures to ensure responsible AI deployment.
• Model Development: Design and implement AI/ML models, algorithms, and pipelines. 
• Collaboration: Work with data scientists, engineers, and business stakeholders to deliver AI-driven solutions. 
• Optimization & Evaluation: Continuously monitor and improve AI systems for accuracy and efficiency. 
• Compliance & Ethics: Ensure AI solutions adhere to ethical standards and regulatory requirements (GDPR, fairness, bias mitigation). 
• Leadership: Guide cross-functional teams and mentor junior engineers in AI best practices
  •  Security & Compliance: Data anonymization, secure model deployment, bias detection