Description

Key Responsibilities

AI Solution Architecture

· Architect comprehensive end-to-end AI systems including: o Advanced RAG (Retrieval-Augmented Generation) pipelines Multi-stage retrieval and re-ranking architectures Agent orchestration frameworks coordinating multiple specialized agents Multi-model AI integrations leveraging model-specific strengths Design solutions with modularity, extensibility, scalability, and operational excellence to support evolving business requirements. AI Engineering Standards & Optimization · Define enterprise standards for: o Prompt engineering Prompt templates and versioning Testing methodologies Evaluation frameworks Establish performance optimization strategies covering: o Model selection criteria Caching patterns Resource utilization Cost optimization Production Deployment & Reliability · Lead deployment of AI solutions into production environments with: o Comprehensive observability Logging and tracing Reliability engineering practices Graceful degradation mechanisms Circuit breaker implementation Real-time monitoring dashboards Automated alerting Incident response procedures Ensure AI services meet stringent service-level objectives and enterprise reliability expectations. Data & Retrieval Architecture · Design scalable data ingestion frameworks that process: o Structured data sources Unstructured documents Real-time event streams Develop: o Vector database architectures Hybrid search capabilities Data preprocessing pipelines Data quality monitoring frameworks Ensure high-quality inputs for AI systems through cleansing, enrichment, and governance processes. AI Evaluation & Continuous Improvement · Establish quantitative evaluation frameworks for AI systems. Implement: o A/B testing capabilities Performance benchmarking User feedback analysis Telemetry-based optimization Drive continuous improvements across: o Prompts Retrieval strategies Agent workflows Model configurations Platform & Infrastructure Collaboration · Partner with platform and infrastructure teams to ensure readiness for AI workloads, including: o GPU infrastructure Model serving platforms Feature stores Scalable data storage Networking infrastructure Define requirements for enterprise AI platform capabilities and integration patterns. Technical Leadership & Mentoring · Mentor engineers through: o Architecture reviews Design guidance Code reviews Career development support Promote engineering excellence through: o Best-practice documentation Technical training Communities of practice Foster a culture of responsible and ethical AI development. Responsible AI & Compliance · Ensure AI solutions adhere to enterprise governance and compliance requirements. Maintain documentation of: o System behavior Decision logic Evaluation methodologies Apply responsible AI principles including: o Fairness Transparency Accountability Bias mitigation Support compliance with applicable regulatory and industry requirements. Required Qualifications Experience · 7+ years of software engineering experience with a strong focus on AI/ML engineering. Proven experience building and operating distributed systems at scale. Demonstrated success delivering AI-driven business outcomes and leading large, complex technical initiatives. Education · Bachelor's degree in Computer Science, Engineering, Data Science, or a related discipline. Equivalent practical experience may be considered. Generative AI Expertise · Deep experience designing and deploying production-grade Generative AI solutions including: o Advanced RAG architectures Multi-hop retrieval and reasoning systems Agent orchestration frameworks Tool-using AI agents Memory-enabled AI systems Multi-model AI architectures Conversational AI platforms Enterprise Solution Delivery · Experience leading complex AI initiatives involving multiple cross-functional teams. Ability to translate business objectives into: o Technical solutions AI architectures Delivery roadmaps Experience driving initiatives from concept through production deployment and optimization. Technical Skills Strong hands-on expertise in: · Python FastAPI React Distributed systems Vector databases Embedding models LLM APIs Agent orchestration frameworks Modern cloud-native architectures AI Engineering Best Practices Experience establishing enterprise standards for: · Prompt engineering Version control and testing AI evaluation methodologies Model observability Cost and performance tracking Benchmarking frameworks Data-driven optimization practices Responsible AI & Governance Strong understanding of: · Responsible AI principles Model governance Risk management Model validation Change management Production monitoring Deployment practices in regulated environments Preferred Qualifications · Experience providing technical leadership across organizational boundaries. Strong mentoring and coaching capabilities. Demonstrated ability to collaborate effectively with: o Product Management Data Science Engineering Security Compliance Architecture Business stakeholders Experience in healthcare, life sciences, insurance, or other regulated industries preferred. Primary Skills for TAG Search Must Have · Generative AI Agentic AI RAG Architecture AI Agents / Multi-Agent Systems Python FastAPI Vector Databases LLM Integration AI Platform Engineering Production AI Deployment AI Evaluation Frameworks Prompt Engineering Observability & Monitoring Enterprise Architecture Strongly Preferred · React Cloud AI Platforms (Azure/OpenAI preferred) Healthcare Domain Experience Responsible AI / AI Governance Distributed Systems Engineering