Title: AI Software Engineer Location: MI-Farmington Hills ZIP/Postal Code 48331 Job Type Contract Category Software Engineering Req # CHI-49b361c2-06b0-4fef-9800-6e62eaa5d1b3 Pay Rate $54 - $68 (hourly estimate) Job Description: *Hybrid work. Candidates must be located in the Farmington Hills / Metro Detroit, Michigan area. No remote candidates.* JOB DESCRIPTION We are looking for a Lead Software Engineer to join our AI agentic engineering team. You will design and deliver guardrail components across services, define where and how enforcement should occur in systems, and mentor engineers on safe design and defensive programming patterns. You will also architect backend services, APIs, and integrations that apply software engineering discipline to govern how AI-driven systems behave across the engineering workflow. This is a hands-on technical leadership role requiring strong proficiency in Python and TypeScript, a track record of delivering cross-service systems, and the ability to define enforcement patterns that other engineers follow. What You'll Do Design and deliver backend services and APIs that enforce system behavior across multiple services Define where and how enforcement, filtering, and validation should occur within system architectures Build cross-service controls and establish the patterns other engineers implement against Instrument and improve observability across service boundaries — structured logging, metrics, distributed tracing Own shared tooling and platform components with broad organizational reach Mentor engineers on safe design, defensive programming, and failure handling practices Core Capabilities Writes production-quality code with strong proficiency in Python and TypeScript Demonstrates advanced experience working in cloud environments (AWS) Designs and implements scalable backend services and APIs across service boundaries Defines enforcement patterns and interface contracts consumed by other teams Thinks in systems — reasons about cross-service dependencies, failure propagation, and contract stability Communicates defensive design principles clearly to other engineers Shows strong technical curiosity and forms clear, experience-backed opinions What Differentiates This Role This role is focused on how systems behave under misuse, failure, or unexpected inputs — not on building user-facing features. At this level, you are defining where enforcement lives and how it works across services, not just implementing within one. You bring both the technical depth to build these systems and the communication skills to make the patterns repeatable by others. We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. Required Skills & Experience 5–8 years of software engineering experience with strong proficiency in Python and TypeScript Demonstrated experience delivering production systems on AWS (Lambda, Fargate, API Gateway) Experience designing enforcement, filtering, or validation logic that spans multiple services Track record of defining interfaces, contracts, or patterns adopted by other engineers Ability to mentor junior and mid-level engineers on defensive design and safe coding practices Clear written and verbal communication — able to define and document system-level enforcement patterns Experience designing and building agentic workflows or multi-agent systems Familiarity with LLM integration patterns — prompt injection detection, guardrail design, or output filtering Nice to Have Experience with AWS Bedrock — model invocation, guardrail configuration, or AgentCore runtimes Experience with agentic or multi-step workflow systems or Background in platform engineering or developer tooling roles Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.

• Contribute to the prototyping and deployment of AI and Generative AI solutions to support client pursuits, sector use cases and demonstrations. • Develop and test LLM-powered applications and agentic workflows under guidance, including tool or function calling patterns. • Support engineering of data-driven architectures across cloud and on-premises environments. • Assist in developing scalable AI systems with embedded Responsible AI and security controls. • Apply MLOps and LLMOps practices including experimentation tracking, model testing and deployment support. • Build and maintain data pipelines integrating structured and unstructured data sources. • Work with vector databases alongside relational and NoSQL systems. • Support deployment of AI workloads using platforms such as AWS and Azure. • Collaborate with architects, sector teams and engineers to transition proof-of-concepts toward production-ready solutions.

Role Description As a Senior Software Engineer on our Enterprise Retrieval team, you’ll help build the retrieval layer that powers enterprise AI agents at Workato. Your work will let an agent answer “what’s the status of the Acme renewal?” by stitching together a Salesforce opportunity, a call summary, the latest Zendesk ticket, a Jira blocker, and a SharePoint contract — all in one ranked, permission-aware response. This is the kind of problem where classical Information Retrieval, dense vector retrieval, knowledge graphs, and LLM-driven reasoning all collide. You’ll work across heterogeneous content (docs, tickets, tasks, CRM records, call transcripts, chat threads), heterogeneous permissions (every source has its own ACL model), and very real freshness constraints (yesterday’s answer is often wrong). It’s a hands-on Senior IC role for someone who wants to go deep on retrieval quality and see their work directly shape how thousands of enterprises put AI agents to work. In this role, you will also be responsible to: - Build a unified retrieval layer across enterprise systems — Google Drive, SharePoint, Confluence, Jira, Asana, Zendesk, Freshdesk, Salesforce, Notion, and more — exposing a clean, agent-friendly interface. - Design hybrid retrieval pipelines that combine lexical (BM25), dense vector, and structured (SQL/graph) retrieval, with smart re-ranking tuned for cross-source results. - Engineer ingestion and freshness pipelines that incrementally sync millions of documents, tickets, tasks, and CRM records with low end-to-end latency and predictable cost. - Own permission-aware retrieval (ACL preservation) — make sure the engine never returns a document a user (or their agent) isn’t entitled to see, mirroring source-system permissions exactly. - Build query understanding for agents — intent parsing, entity linking across systems (a “customer” in Salesforce is the same as in Zendesk), and LLM-assisted query rewriting and decomposition. - Design chunking and embedding strategies tailored to each content type — long docs, short tickets, threaded conversations, structured records, call transcripts. - Build evaluation and experimentation harnesses (NDCG, MRR, recall@k, faithfulness, citation accuracy) for both retrieval and end-to-end agent answers. - Ship production-grade, observable systems with strong SLOs on latency, freshness, recall, and cost — and the dashboards/tracing to prove it. - Mentor teammates and raise the bar on retrieval architecture, evaluation rigor, and engineering craft. Qualifications - 3-5 years building production search, retrieval, knowledge-base, or recommendation systems. - Strong proficiency in at least one modern backend language — Python, Go, Java, or similar. - Hands-on experience with search engines such as OpenSearch, Elasticsearch, Solr, or Vespa, including index design and analyzers. - Solid grounding in IR fundamentals: TF-IDF, BM25, learning-to-rank, query parsing, and relevance evaluation. - Working experience with vector search and embeddings — FAISS, pgvector, Pinecone, Weaviate, Qdrant, Milvus, or native Elasticsearch/OpenSearch kNN. - Experience designing or contributing to RAG pipelines and semantic search systems in production. - Familiarity with modern NLP/LLM tooling: transformer embeddings, cross-encoder re-rankers, prompt engineering, and frameworks like LangChain, LlamaIndex, or Haystack. - Comfortable building integrations against SaaS APIs (REST/GraphQL/webhooks), handling OAuth, rate limits, pagination, and incremental sync. - Solid intuition for ACL/permission models in enterprise systems (Drive sharing, SharePoint groups, Jira project roles, Salesforce sharing rules, etc.) and how to preserve them in a retrieval layer. - Strong SQL skills, comfort with NoSQL/document stores, and experience with large-scale distributed systems. - Familiarity with cloud platforms (AWS, GCP, or Azure), containerization, and CI/CD. Requirements - Clear communicator who can explain technical trade-offs to engineers, PMs, and executives alike. - Collaborative — you partner naturally with ML, product, security, and platform teams. - Quality-obsessed and detail-oriented, with an instinct for measurable outcomes over vibes. - Self-directed; comfortable taking an ambiguous problem from zero to shipped. - Genuinely curious about the hard, interesting problems hiding inside enterprise retrieval — heterogeneity, permissions, freshness, and trust. Nice to Have - Experience with knowledge graphs, entity resolution, or cross-source identity linking. - Experience tuning or fine-tuning embedding models (sentence-transformers, BGE, E5, etc.) for domain-specific retrieval. - Exposure to agentic AI patterns — tool use, function calling, MCP, or multi-step retrieval planning. - Experience with streaming/real-time ingestion (Kafka, Flink, Spark) and cost optimization at scale. - Background in enterprise search, e-discovery, observability, or DLP — anywhere you’ve had to handle messy multi-source content with strict access controls. - Open-source contributions, published research, or writing on retrieval, IR, or applied ML.

• Design, implement, and own multi-step agentic flows using LangChain, LangGraph, and similar. • Build tools, agents, and chains that connect to live data sources (APIs, databases, time-series feeds) • Integrate LLMs (Claude, GPT, Gemini, etc') into reliable, observable pipelines • Own prompt engineering, context management, and output validation • Experience working in large-scale and big data environments • Deploy and monitor AI services on AWS or Azure • Instrument flows with logging and tracing so we can debug and improve them • Build React interfaces that surface AI outputs: summaries, alerts, recommendations, and interactive co-pilot features • Connect frontend components to streaming AI responses • Write clean, maintainable TypeScript code