Senior AI Engineer

This position offers you the opportunity to join a fast-growing technology organization that is redefining productivity paradigms in the software engineering industry. Thanks to our flexible, distributed model of global operation and the high caliber of our experts, we have enjoyed triple digit growth over the past five years, creating amazing career opportunities for our people.If you want to accelerate your career working with like-minded subject matter experts, solving interesting problems and building the products of tomorrow, this opportunity is for you.Parser is seeking an experienced AI Engineer to join our innovative team. The ideal candidate will have a strong background in building production-ready AI systems, with a focus on agentic AI and large language models.Must-Have SkillsPython expert with production experience building APIs/services (FastAPI/Flask/Django), REST/GraphQLHands‑on experience with agentic AI: LangChain and LangGraph (stateful workflows, planners, tools, memory, error handling, retries, concurrency)Proficiency in A2A (agent-to-agent) interaction patterns and multi‑agent orchestration in production (e.g: AutoGen, CrewAI, etc).Experience with MCP (Model Context Protocol): building/consuming MCP tools/servers; integrating model‑context tools safelyLLM integration across major providers (OpenAI, Anthropic, Azure OpenAI); function/tool calling, structured outputs (JSON Schema), prompt engineeringExpertise in Retrieval‑augmented generation (RAG): embeddings, chunking, indexing; vector databases (Pinecone, Weaviate, Qdrant, FAISS); reranking basicsFull‑stack delivery: React/Next.js with TypeScript and component libraries; secure auth flows; building internal dashboardsCloud and DevOps: Docker, CI/CD (GitHub Actions/GitLab), at least one major cloud (AWS/Azure/GCP), secrets management, environment promotionSecurity/compliance for enterprise: OAuth/SSO, RBAC, audit logging, PII handling, encryption, least‑privilegeTesting and observability: pytest, integration/e2e tests, tracing/logging (OpenTelemetry), metrics, feature flagsAI developer tools proficiency: GitHub Copilot and QDev (or similar), prompt/version management, code review with AIStrong Git workflows, code reviews, documentation; Agile delivery in cross‑functional teamsNice‑to‑Have SkillsAdditional agent frameworks and patterns: AutoGen, DSPy, LlamaIndex; workflow DAGs, checkpointing, long‑term memoryAdvanced RAG: hybrid search, GraphRAG/knowledge graphs (Neo4j), rerankers (cross‑encoders), query planning, guardrailsModel serving and optimization: vLLM, Ray Serve, Triton, Ollama; caching, batching, cost/latency trade‑offsEvals and quality: LangSmith, RAGAS, DeepEval, custom eval harnesses; offline/online metrics, human‑in‑the‑loop reviewEvent‑driven and async systems: Kafka/PubSub, Celery/Redis queues, websockets/SSE, streaming tokensDatastores and caching: Postgres, Redis, Elastic/OpenSearch; schema design, migrations, performance tuningMLOps: MLflow/W&B, experiment tracking, model registry, feature stores, data versioning (DVC)Infra as code and platform: Terraform/Pulumi, Kubernetes, Helm; on‑prem/VPC deployments, network/security hardeningFrontend depth: design systems, accessibility, data viz (ECharts/D3), performance profilingCompliance and privacy: GDPR, SOC 2, ISO 27001, audit readiness in AI systemsEnterprise integrations: Microsoft 365/SharePoint, ServiceNow, Slack/Teams bots, SSO/SCIMDomain context: professional services/finance workflows, document/intelligence automationTooling ecosystem: building custom MCP tools, code‑interpreter/sandboxed execution, browser and SaaS connectorsIf you're passionate about pushing the boundaries of AI technology and have the skills to match, we'd love to hear from you. Join Parser and help shape the future of intelligent systems#J-18808-Ljbffr