Senior AI Software Engineer

As a Lead AI Software Engineer youll provide technical leadership for a workstream building complex multi-agent AI systems owning the architecture setting the technical bar and acting as the bridge between engineering product security and compliance.

Key Responsibilities

• Provide technical leadership for a team/workstream building multi-agent and multi-model AI systems
  

• Own architecture decisions and lead design reviews (Architecture Decision Records) for key AI initiatives
  

• Architect scalable full-stack AI solutions spanning data engineering model training/fine-tuning and LLMOps
  

• Drive cost and performance optimization for AI systems running at enterprise scale
  

• Partner cross-functionally with Product Data Science Security and Compliance to shape solution design
  

• Evaluate and select AI tools frameworks and vendors for the team
  

• Mentor and upskill engineers fostering technical growth within the team
  

• Balance hands-on technical contribution with team-level planning and delivery accountability
  

Skills & Key Competencies

Must-Have Technical Skills

• Technical leadership of AI engineering workstreams or a small team
  

• Architecting complex multi-agent/multi-model AI systems at scale
  

• Full-stack AI expertise: data engineering model training/fine-tuning LLMOps infrastructure
  

• Owns architecture decisions and design reviews (ADRs)
  

• Cross-functional partnership with Product Data Science Security and Compliance
  

• Cost and performance optimization of AI systems in production
  

• Mentors and upskills engineers
  

Good-to-Have Technical Skills

• Experience building/owning internal AI platforms or agent frameworks
  

• Vendor and AI tool evaluation/selection
  

• Contribution to AI governance and compliance frameworks (GDPR SOC 2 HIPAA-aware design)
  

• Experience in regulated or highly audited environments
  

Key Competencies

• Balances big-picture architectural thinking with a willingness to get into the weeds
  

• Decisive under ambiguity can make an 80%-confidence call and move the team forward
  

• Strong stakeholder management across technical and non-technical audiences
  

• Builds trust quickly with both engineers and cross-functional partners
  

• Invests genuinely in growing people not just shipping features
  

• Holds a high technical bar without being a bottleneck

  Required Skills:

  Must-Have Technical Skills Hands-on experience architecting microservice-based systems at enterprise scale including service decomposition database-per-service design and inter-service communication (REST gRPC event-driven messaging) Hands-on experience applying distributed-systems patterns Saga CQRS Outbox Circuit Breaker and Service Mesh across multiple product lines Hands-on experience designing systems for petabyte-scale data across multiple product lines: partitioning/sharding strategies distributed storage and query performance at scale Hands-on experience architecting enterprise GenAI platforms: foundation model selection/evaluation LLM gateway and routing architecture and token-economics-based cost optimization Hands-on experience architecting production RAG systems: hybrid retrieval cross-encoder reranking query decomposition GraphRAG and RAG evaluation frameworks (e.g. RAGAS) Hands-on experience architecting enterprise multi-agent systems using supervisor/worker orchestration patterns open interoperability standards (MCP A2A) and agent governance controls Hands-on experience setting multi-year technology vision and translating it into an executable platform roadmap Hands-on experience evaluating and piloting emerging AI/GenAI technologies for enterprise adoption with a track record of recommendations that held up in production Hands-on experience leading customer-facing technical architecture discussions and acting as a trusted technical advisor/consultant on AI solution design Hands-on experience establishing AI governance frameworks aligned to recognized standards (ISO/IEC 42001 NIST AI RMF) at the enterprise level Hands-on experience architecting security controls for AI systems: secrets management network segmentation threat modeling and secure-by-design practices Hands-on experience ensuring AI systems meet privacy and regulatory compliance requirements across regions (GDPR/UK GDPR Indias Digital Personal Data Protection Act (DPDPA) and DPDP Rules EU AI Act and sector frameworks such as HIPAA/CCPA where relevant) Strong track record mentoring Principal and Lead engineers and building technical leadership capacity across the organization