Hiring! AI Architect Generative AI & Agentic AI from Healthcare Domain Only

Role Overview

The AI Architect will lead the architecture, design, and production rollout of enterprise-grade AI solutions, with a focus on Generative AI (GenAI), Retrieval-Augmented Generation (RAG), and agentic AI systems. The role translates business needs into secure, scalable, reliable, cost-effective architectures and establishes reusable architecture patterns, engineering standards, and governance practices. The architect partners with product, data, platform, security, engineering, and business leaders to take AI capabilities-from conversational assistants and document intelligence to workflow automation and decision support-from experimentation into production.

Key Responsibilities

• Define enterprise AI architecture strategy, reference architectures, technology standards, and a pragmatic roadmap aligned to business outcomes.
• Design end-to-end AI solutions spanning data ingestion, model access, retrieval, orchestration, APIs, user experience, deployment, monitoring, and governance.
• Architect production GenAI, RAG, and agentic workflows with appropriate controls for quality, latency, availability, scalability, and cost.
• Assess and select foundation models, embedding models, vector/search technologies, agent frameworks, cloud services, and deployment patterns; document trade-offs and decisions.
• Establish reusable components, architecture patterns, design reviews, engineering guidelines, and platform capabilities that accelerate delivery across teams.
• Lead technical discovery, prototypes, architecture spikes, and production readiness reviews; guide teams through implementation and operational handover.
• Partner with security, privacy, legal, risk, and compliance stakeholders to implement responsible AI, data protection, access controls, auditability, and risk-based safeguards.
• Define evaluation and observability practices for AI systems, including quality, groundedness, safety, drift, latency, usage, and cost metrics.
• Mentor engineers and technical leads; communicate architecture decisions and risks to executive, product, and engineering stakeholders.

Mandatory Skills & Experience

• Enterprise AI & GenAI Architecture
• Demonstrated experience designing and deploying production-grade GenAI applications such as conversational assistants, summarization, document intelligence, knowledge assistants, and decision-support systems.
• Strong understanding of LLM behavior and constraints: tokenization, context windows, prompting, structured outputs, tool/function calling, model selection, and failure modes.
• Experience designing inference and serving patterns for latency, throughput, and cost, including streaming, batching where appropriate, caching, asynchronous processing, and response chunking.
• Ability to evaluate build-versus-buy, hosted-versus-self-hosted, and model/platform trade-offs.
• Retrieval-Augmented Generation (RAG) & Knowledge Systems
• Hands-on architecture experience with ingestion, parsing, metadata enrichment, chunking, embeddings, indexing, retrieval, reranking, context assembly, and grounded generation.
• Knowledge of dense retrieval, BM25, hybrid search, metadata filtering, semantic ranking, query rewriting, multi-hop retrieval, and retrieval evaluation.
• Experience with vector databases/search platforms such as pgvector, Pinecone, Weaviate, Milvus, Qdrant, Chroma, Elasticsearch/OpenSearch, or cloud-native equivalents.
• Design for enterprise knowledge sources, document permissions, freshness/synchronization, provenance, feedback loops, and measurable retrieval quality.
• Agentic AI & Workflow Architectures
• Experience architecting tool-using agents and multi-agent/workflow systems, with clear boundaries for planning, execution, delegation, state, memory, and human approval.
• Familiarity with frameworks such as LangGraph, LangChain, Microsoft Agent Framework / AutoGen, Semantic Kernel, and CrewAI; select frameworks based on use case rather than framework preference.
• Design reliable orchestration using state machines, durable workflows, event-driven patterns, retries, timeouts, idempotency, and human-in-the-loop controls.
• Knowledge of tool/function calling, MCP (Model Context Protocol), API integration, agent-to-agent communication patterns, and secure tool authorization.
• Understand planning-and-execution, reflection/critique patterns, memory approaches, and the limits of autonomous behavior; use bounded autonomy and deterministic controls for critical actions.
• Cloud, Platform & Infrastructure (AWS, Azure, Google Cloud Platform)
• Architecture experience on at least one major cloud platform (AWS, Microsoft Azure, or Google Cloud); working familiarity with the other platforms and ability to map equivalent services.
• AWS: Amazon Bedrock, SageMaker, S3, Lambda, EKS, IAM, CloudWatch, and relevant data/search services.
• Azure: Azure AI Foundry, Azure Machine Learning, Azure OpenAI where applicable, Azure Kubernetes Service (AKS), Functions, Entra ID, Azure Monitor, and data/search services.
• Google Cloud Platform: Vertex AI, Gemini, BigQuery, Cloud Storage, GKE, Cloud Run, IAM, and Vertex AI Search or relevant retrieval services.
• Experience with cloud networking, identity and access management, secrets, encryption, private connectivity, environment separation, resilience, and cost controls.
• Containerization and deployment experience with Docker and Kubernetes or managed equivalents; infrastructure-as-code familiarity (Terraform, CloudFormation, Bicep, or similar).
• AI/ML Engineering, APIs & Data
• Strong Python skills and practical experience with production software engineering practices; familiarity with REST APIs, event-driven integration, and service contracts.
• Knowledge of ML lifecycle concepts: data preparation, training/fine-tuning, model registry, deployment, versioning, monitoring, and rollback.
• Experience with data pipelines and enterprise data architectures, including SQL, object storage, lakehouse/warehouse patterns, and batch/stream processing.
• Familiarity with PyTorch or TensorFlow, Hugging Face ecosystem, and model-serving options such as managed endpoints, vLLM, or equivalent technologies.
• Knowledge of CI/CD, automated testing, Git-based workflows, observability, and production operations.
• Security, Governance, Evaluation & Responsible AI
• Design defenses against prompt injection, jailbreaks, sensitive-data exposure, insecure tool use, data exfiltration, and unsafe model outputs.
• Apply least privilege, tenant/data isolation, identity-aware retrieval, encryption, secrets management, retention controls, and audit logging.
• Define AI evaluation strategies for task success, groundedness, relevance, factuality, safety, bias, robustness, and regression testing; combine automated and human evaluation.
• Familiarity with LLM/agent observability and tracing tools such as Langfuse, LangSmith, OpenTelemetry, cloud-native monitoring, or equivalents.
• Working knowledge of responsible AI and risk frameworks such as NIST AI RMF, ISO/IEC 42001, and applicable privacy/security obligations; experience in regulated domains is beneficial.

Good-to-Have Skills (Differentiators)

• LLM customization and optimization: fine-tuning, PEFT, LoRA/QLoRA, quantization, distillation, prompt tuning, and inference optimization.
• Model benchmarking and evaluation experience; awareness of task-specific metrics and limitations of generic metrics such as BLEU, ROUGE, or perplexity for conversational/grounded systems.
• Experience with multimodal AI (text, image, audio, video), speech/voice interfaces, vision pipelines, or real-time applications.
• Experience with federated/multi-source RAG, enterprise content integrations (CRM, document management, wikis, intranet), and permission-aware indexing.
• Exposure to open and commercial models/providers such as OpenAI, Anthropic, Google Gemini, Meta Llama, Mistral, Cohere, and other suitable model families.
• Experience with agent memory architectures, durable state, workflow engines, and production agent analytics.