| Job Description: |
"8+ years of total software engineering and technical architecture experience
3+ years of hands-on experience designing and deploying enterprise Gen AI or agentic workflows into production environments
Experience conducting client-facing technical workshops, architecting solutions for RFPs, and translating complex business requirements into robust designs
Programming: Advanced Python (Pandas, PyTorch, Hugging Face, Scikit-Learn)
Frameworks: LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen
Cloud Platforms: AWS (Bedrock), Azure (Azure OpenAI), or GCP (Vertex AI)
Databases: Vector DBs (ChromaDB, Pinecone, FAISS, Weaviate)
Microservices: FastAPI, Flask, Spring Boot, REST APIs
DevOps / MLOps: Docker, Kubernetes, CI/CD pipelines, MLflow, model monitoring tools "
"Key Responsibilities
1. Architecture & Solution Design
System Design: Define end-to-end architectures for complex Gen AI solutions, including Retrieval-Augmented Generation (RAG) pipelines, multimodal models, and autonomous multi-agent systems
Reusable Patterns: Establish reusable architectural patterns, blueprint standards, and engineering templates to accelerate AI adoption and eliminate redundant engineering efforts
Integration Strategy: Design secure, cloud-native microservices and APIs (e.g., FastAPI, REST) to seamlessly connect AI orchestrators with enterprise data backends and legacy systems
2. Model & Agent Strategy
Orchestration & Agentic Systems: Architect and build multi-agent, goal-driven, autonomous AI systems utilizing frameworks such as LangGraph, CrewAI, AutoGen, or LlamaIndex
Model Lifecycle & Strategy: Lead the evaluation, selection, and fine-tuning strategy for proprietary APIs (e.g., OpenAI, Anthropic, Gemini) and open-source models (e.g., Llama, Mistral) based on performance, latency, and cost
Prompt Engineering: Standardize advanced prompt engineering frameworks (e.g., chain-of-thought, few-shot prompting) to ensure deterministic and scalable model behavior
3. LLMOps, Infrastructure, & Data
Infrastructure Sizing: Define cloud infrastructure scaling models, GPU/TPU sizing strategies, and orchestration layouts (utilizing Docker, Kubernetes/OpenShift)
Vector Databases: Architect robust information retrieval utilizing vector search databases (e.g., Pinecone, Milvus, Weaviate, FAISS)
Operational Pipelines: Implement end-to-end GenAIOps/LLMOps pipelines for automated model deployment, performance monitoring, drift detection, and observability
4. Governance, Security, & Compliance
Responsible AI: Embed governance, security, and compliance guardrails by design into all AI architectures, ensuring protection against prompt injection, data leakage, and PII exposure
Quality Assurance & Testing: Implement evaluation frameworks to systematically test LLM accuracy, reduce hallucinations, and validate solution architectures "
Role Descriptions:
Essential Skills: Gen AI Architect
Desirable Skills:
Keyword:
Skills: AI & Gen AI - Products & Tools
Experience Required: 6-8 |
