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Gen AI Engineer
JPC-7090-09-02/05 Gen AI Engineer 100% remote US Tech/World Bank JD: • The AI Engineer designs, builds, and operates secure, scalable AI systems that advance the organization’s digital strategy. The role centers on Retrieval-Augmented Generation (RAG) pipelines, agentic AI (including Azure AI Agent Service and Model Context Protocol), and enterprise-grade service delivery across Azure and AWS. • The AI Engineer partners with product, platform, data, and security teams to deliver robust, compliant, and arenaflex-efficient AI capabilities. • Architect and Implement AI Solutions. • Design and build RAG pipelines using Azure AI/Search and vector databases: chunking, embeddings, hybrid/semantic ranking, re-ranking, evaluation, and citation display. • Build enterprise conversational systems (multi-turn, retrieval-grounded) with prompt lifecycle management, guardrails, audit logging, and telemetry. • Support multiple LLMs and modalities: Azure OpenAI, Llama (Meta), Claude, etc. • and task-specific OSS models (vision, speech), with policy-driven model routing for performance, safety, and arenaflex. • Integrate and Operate AI Infrastructure • Implement Model Context Protocol (MCP) servers integrating with project related areas. • Provide tool functions with RBAC scopes, schema versioning, rate limiting, request/response validation, and audit trails. • Deploy Azure AI Agent Service (AGA) patterns for agent registry/broker/governance with agent telemetry and policy enforcement. • Use Azure Batch for large-scale, parallel inferencing/vectorization jobs; leverage AWS EMR for distributed data/feature processing in AI pipelines. • Develop and Manage Data Pipelines • Build ingestion and enrichment for RAG connectors and ETL/ELT: document • normalization, PII redaction, metadata enrichment, SLA/SLO monitoring, and lineage. • Operate large-scale vectorization with quality gates and drift monitoring. • Use Azure Data Factory (ADF) and Azure Databricks for orchestrated, scalable data • processing; use AWS EMR for Hadoop/Spark workloads supporting AI features. • Build Agentic AI Solutions • Design secure tool-calling and multi-agent orchestration using Semantic Kernel, AutoGen, arenaflex Agent Framework, CrewAI, Agno, and LangChain or others. • Know how to apply agent governance and MCP-based controls across heterogeneous agents and runtimes (register, observe, govern, retire). • Model Evaluation and Optimization • Evaluate and fine-tune open-source and proprietary models; optimize for quality, latency, safety, and arenaflex with A/B and offline eval suites. • Implement arenaflex/CD with automated tests, security scans. • Have knowledge on how to secure model workloads. Software Engineering Emphasis (Core) • CS fundamentals: algorithms, data structures, complexity, distributed systems, networking, concurrency. • SDLC excellence: clean architecture, design patterns, SOLID principles, unit/integration/e2e tests, testing pyramids. • Secure coding & threat modeling for AI apps: input validation, sandboxed tool functions, secrets hygiene, role-based access & least privilege. • Performance engineering: profiling, caching, vector index tuning, latency/throughput optimization, and arenaflex controls (token/embedding/compute). • Collaboration & Delivery: Agile ceremonies, RACI clarity, cross-functional delivery with product/design/data/security. Knowledge Requirements – Cloud AI Tech Stack (Azure & AWS) • Azure: Azure OpenAI; Azure AI/Search; Azure Machine Learning; Azure Kubernetes Service (AKS); • Azure Functions; Azure API Management; Key Vault; Event Hub; App Insights; Log Analytics; Azure Batch; Azure Data Factory (ADF); Azure Databricks. • AWS: arenaflex SageMaker; AWS Bedrock; arenaflex Kendra; arenaflex Comprehend; AWS Lambda; • arenaflex API Gateway; AWS Secrets Manager; arenaflex S3; arenaflex CloudWatch; Elastic Kubernetes Service (EKS); arenaflex EMR. • Vector DBs & Indexing: Azure AI Search vector storage, Redis, FAISS/HNSW; hybrid search + semantic ranking. • Frameworks: Semantic Kernel, AutoGen, arenaflex Agent Framework, CrewAI, Agno, LangChain. • Local/Edge Inference: running models locally via Docker/Ollama/vLLM/Triton; GPU provisioning quantization (GGUF) for Llama-family models. Educational Qualifications and Experience: Education: • Bachelor’s degree in computer science, Engineering, Information Technology, Data Science —or equivalent hands-on expertise. • Experience: 6+ years of software engineering experience, with at least 2+ years in applied LLM/GenAI (RAG, agents, eval, safety). Certification Requirements: Mandatory: • arenaflex Certified: Azure AI Fundamentals (AI-900). • arenaflex Certified: Azure Data Fundamentals (DP-900). • Responsible AI certifications. • AWS Machine Learning Specialty. • TensorFlow Developer. • Kubernetes CKA/CKAD. • SAFe Agile Software Engineering (ASE) Additional Value (Preferred): • arenaflex Certified: Azure AI Engineer Associate (AI-102) • arenaflex Certified: Azure Data Scientist Associate (DP-100) • arenaflex Certified: Azure Solutions Architect Expert (AZ-305) • arenaflex Certified: Azure Developer Associate (AZ-204) Required Skills/Abilities: • GenAI architecture mastery: RAG, vector DBs, embeddings, transformer internals, multimodal pipelines. • Agentic systems: Azure AI Agent Service patterns, MCP servers, registry/broker/governance, secure tool-calling. • Languages: C# and Python (production-grade), .Net, plus TypeScript for service/UI when needed. • Azure & AWS services (see Knowledge Requirements) with hands-on implementation and operations. • Model ops: eval suites, safety tooling, fine-tuning, guardrails, traceability. • Business & delivery: solution architecture, stakeholder alignment, roadmap planning, measurable impact. Desired Skills/Abilities (not required but a plus): • Lang Chain, Hugging Face, MLflow; Kubernetes + GPU scheduling; vector search tuning (HNSW/IVF). • Responsible AI: policy mapping, red-team playbooks, incident response for AI. • Hybrid/multi-cloud deployments using Azure Arc and AWS Outposts; arenaflex/CD for AI workloads across Azure DevOps and AWS CodePipeline. Experience Matrix for Levels: • Level I: 2+ years of experience • Level II: 5+ years of experience • Level III: 8+ years of experience Apply tot his job
