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 Skills

Python expert with production experience building APIs/services (FastAPI/Flask/Django), REST/GraphQL
Hands‑on experience with agentic AI:
Lang Chain and Lang Graph (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: Auto Gen, CrewAI, etc).

Experience with MCP (Model Context Protocol): building/consuming MCP tools/servers; integrating model‑context tools safely
LLM integration across major providers (OpenAI, Anthropic, Azure OpenAI); function/tool calling, structured outputs (JSON Schema), prompt engineering
Expertise in Retrieval‑augmented generation (RAG): embeddings, chunking, indexing; vector databases (Pinecone, Weaviate, Qdrant, FAISS); reranking basics
Full‑stack delivery:
React/Next.js with Type Script and component libraries; secure auth flows; building internal dashboards
Cloud and Dev Ops:
Docker, CI/CD (Git Hub Actions/Git Lab), at least one major cloud (AWS/Azure/GCP), secrets management, environment promotion
Security/compliance for enterprise: OAuth/SSO, RBAC, audit logging, PII handling, encryption, least‑privilege
Testing and observability: pytest, integration/e2e tests, tracing/logging (Open Telemetry), metrics, feature flags
AI developer tools proficiency:
Git Hub Copilot and QDev (or similar), prompt/version management, code review with AI
Strong Git workflows, code reviews, documentation;
Agile delivery in cross‑functional teams

Nice‑to‑Have Skills

Additional agent frameworks and patterns:
Auto Gen, DSPy, Llama Index; workflow DAGs, checkpointing, long‑term memory
Advanced RAG: hybrid search, Graph

RAG/knowledge graphs (Neo4j), rerankers (cross‑encoders), query planning, guardrails
Model serving and optimization: vLLM, Ray Serve, Triton, Ollama; caching, batching, cost/latency trade‑offs
Evals and quality:
Lang Smith, RAGAS, Deep Eval, custom eval harnesses; offline/online metrics, human‑in‑the‑loop review
Event‑driven and async systems:
Kafka/Pub Sub, Celery/Redis queues, websockets/SSE, streaming tokens
Data stores and caching:
Postgres, Redis, Elastic/Open Search; schema design, migrations, performance tuning
MLOps: 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 hardening
Frontend depth: design systems, accessibility, data viz (ECharts/D3), performance profiling
Compliance and privacy: GDPR, SOC 2, ISO 27001, audit readiness in AI systems
Enterprise integrations:
Microsoft 365/SharePoint, Service Now, Slack/Teams bots, SSO/SCIM
Domain context: professional services/finance workflows, document/intelligence automation
Tooling ecosystem: building custom MCP tools, code‑interpreter/sandboxed execution, browser and SaaS connectors

If 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!
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