Senior AI Engineer, Agentic Systems
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Job Description
Senior AI Engineer at AI Technology Partners designing agentic systems solutions for enterprises. Leading architecture and integration efforts for robust, secure AI deployments while collaborating with enterprise clients.
Responsibilities:
- Design multi-agent architectures with robust state management, memory, and routing.
- Choose and implement leading frameworks such as LangGraph/LangChain Agents, Microsoft AutoGen, CrewAI, LlamaIndex Agents, Semantic Kernel, or Haystack Agents—and justify trade-offs.
- Build modular components (planners, tool registries, policy guards, evaluators) that are reusable across clients and domains.
- Integrate enterprise tools and data sources via function/tool calling, webhooks, and event-driven flows (Queues/Service Bus/Functions).
- Implement retrieval-augmented generation (RAG) patterns with vector stores (Azure AI Search, pgvector, MongoDB Atlas, Pinecone, Weaviate, Milvus) and structured knowledge (SQL/Graph).
- Add deterministic fallbacks, circuit breakers, and caching to keep latency and cost predictable.
- Define SLIs/SLOs for agent runs; implement tracing, metrics, and logging (e.g., Langfuse + OpenTelemetry) and build dashboards for run-level analytics.
- Create evaluation harnesses (automatic + human-in-the-loop) using tools such as Ragas, DeepEval, promptfoo to measure groundedness, task success, safety, and cost.
- Productionize with CI/CD, environment promotion, feature flags, and canary strategies; optimize cost-per-task and time-to-success.
- Enforce content and safety policies (redaction, classification, guardrails) with policy-as-code; implement role/tenant isolation and data minimization.
- Collaborate with security teams to align to ISO 27001/SOC 2/NIST/HIPAA/GDPR contexts; deliver audit-ready evidence for agentic workflows.
- Build privacy-first patterns (no data exfiltration by default, least-privilege tool access, secure prompt/trace storage).
- Work directly with enterprise client teams to translate business processes into agentic designs; present trade-offs and proofs-of-value that lead to production.
- Partner with solution leads to create domain-specific agents (e.g., RFP assist, incident RCA drafting, knowledge ops) and reusable templates.
Requirements:
- 5–8+ years in software/platform engineering with recent production LLM applications (not just prototypes).
- Hands-on expertise with agentic frameworks (one or more of: LangGraph/LangChain Agents, AutoGen, CrewAI, LlamaIndex Agents, Semantic Kernel, Haystack Agents) and tool/function-calling patterns.
- Strong RAG engineering across vector DBs, chunking/embedding strategies, metadata/search ranking, and grounding techniques.
- Proven track record building observable, cost-aware, and secure LLM systems (tracing, evals, guardrails, secrets/IAM, PII handling).
- Solid software engineering fundamentals: Python/TypeScript, async patterns, APIs, testing, CI/CD, containerization.
- Clear communicator who can interface with clients and write crisp technical docs.
- Azure-first experience (Azure OpenAI, Azure AI Studio, Azure Functions/Container Apps/AKS, Private Link/VNet, Key Vault, Entra ID) is nice to have.
- Cross-cloud exposure (AWS/GCP) and hybrid integrations; experience with enterprise connectors (SharePoint/OneDrive, ServiceNow, Salesforce).
- Experience with structured output, constrained decoding, JSON Schemas, and program-of-thought planning.
Benefits:
- Challenging work on meaningful, production agentic systems for enterprise clients.
- Learning & sharing culture with deep dives, brown bags, and support for certifications/publication.
- Inclusive, flexible workplace —bring your whole self; work where you do your best thinking.
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