Job description

Who You’ll Work With At Slalom, we co-create modern technology and software products with clients who are ready to accelerate their digital product development. We imagine how things can be made better, then set out to realize what’s possible — driving innovation with quality, resilience, and purpose. By blending design, product engineering, analytics, and automation, we build the custom-built software and data products of tomorrow. As a member of Intelligence Engineering, you’ll design and deliver innovative AI/ML and agentic AI solutions as part of intelligent products and automating / re-envisioning human workflows on Amazon Web Services, Azure, and Google Cloud. This includes architecting multi-agent systems, LLM-powered autonomous workflows, retrieval-augmented generation (RAG) pipelines, and enterprise-grade AI governance frameworks using cutting-edge orchestration frameworks, tool-use protocols, and cloud-native AI services. You’ll help clients move from isolated proofs of concept to secure, scalable, observable systems that create real business value, while helping craft projects in their initial phases and delivering them with a team. We are open to hiring Senior Engineer, Architect, or Senior Architect level across the Central region. We offer a flexible working environment to balance the need to work independently, with some days that may require in-person collaboration at our office. What You’ll Do • Provide thought leadership on AI/ML, Generative AI, and Agentic AI internally and with clients, while contributing to a culture of collaboration, learning, and curiosity • Design end-to-end agentic AI architectures including planning loops, memory management, tool integration, and agent coordination patterns • Architect multi-agent orchestration systems using frameworks such asStrands Agents SDK,OpenAI Agents SDK, Google ADK, Lang Graph or similar for autonomous reasoning, decision-making, and task execution • Design and implement Model Context Protocol (MCP) server integrations for tool use, data access, and cross-system interoperability with enterprise systems • Build advanced retrieval-augmented generation (RAG) systems including vector databases, embedding strategies, chunking optimization, hybrid search, re-ranking, and multi-source data synthesis • Design and deliver AI and ML solutions across AWS, Azure, and GCP, using the right mix combination of cloud-native data services, ML tooling, LLM platforms, and software engineering practices • Build in Python and, where useful, other languages to deliver machine learning systems, APIs, evaluation harnesses, retrieval pipelines, agent workflows, and production services • Recommend and implement architecture for model and agent pipelines, CI/CD, testing, deployment, observability, andMLOps/LLMOps at scale • Implement evaluation frameworks (e.g., RAGAS, DeepEval, LangSmith) to measure task success rates, tool-call accuracy, and reasoning integrity for GenAI systems • Build guardrails for safety, compliance, and performance monitoring including human-in-the-loop (HITL) approval workflows, escalation policies, and sandbox isolation • Define AI governance frameworks including model risk management, responsible AI practices, regulatory compliance, and authorization boundaries for autonomous decision-making • Explain model and system behavior to both technical and non-technical audiences, including leading deep technical presentations, workshops, and architecture conversations • Collaborate with Product Owners to apply Slalom’s agile process and lead the initiation, delivery, and transition of projects in a client-facing role • Lead and mentor engineers and machine learning practitioners. Lead smaller projects (3 to 5 people) as the technical lead from project initiation to delivery • Build trusted relationships with customers and collaborate across Slalom teams to share learnings and strengthen the broader Intelligence Engineering practice • Will be delivery-focused approximately 85–95% of the time Travel may be required on a limited basis dependent on your client engagement or internal meetings/events. What You’ll Bring • 5+ years of software engineering experience building and deploying production systems; experience with machine learning, applied AI, or intelligent software systems is a plus, with 2+ years focused on generative AI, LLMs, or agentic AI systems • Hands-on experience designing or building multi-agent systems including agent orchestration, tool integration, and autonomous decision-making workflows • Proficiency with at least one agentic AI or workflow framework such as Lang Graph, Strands, AutoGen, CrewAI, Semantic Kernel, OpenAI Agents SDK, Google ADK, or similar • Experience with RAG architectures including vector databases, embeddings, and retrieval optimization, and context management techniques such as chunking, summarization, and memory handling • Experience developing production-ready solutions on at least one major cloud AI platform, such as AWS Bedrock, Azure AI Foundry/OpenAI Service, GCP Vertex AI/Gemini, or Databricks; experience operating and maintaining production environments is a plus • Experience with AI-assisted development tools such as Claude Code, Cursor, Kiro, or similar IDE-based coding agents, including effective use for code generation, refactoring, debugging, and developer workflow acceleration • Strong Python development skills; experience withFastAPI, Flask, or equivalent API frameworks • Experience building ML or AI systems end to end, including data access, feature or retrieval flows, APIs, testing, deployment, and production support • Familiarity with evaluation frameworks, tracing, observability, model behavior analysis, and regression testing for GenAI systems • Understanding of prompt engineering, LLM fine-tuning, chain-of-thought reasoning, and structured output techniques • Recognized as an authority on at least one technical domain (e.g., Agentic Systems, RAG, Multi-Agent Orchestration) with generalist familiarity across AI/ML techniques • Ability to work across new domains and unfamiliar data structures and lead exploratory analysis when requirements are not fully defined • Excellent verbal and written communication skills; ability to lead highly technical presentations • Familiarity with Agile project delivery • (Preferred) Experience with Model Context Protocol (MCP) server development and integration • (Preferred) Experience with MLOps/LLMOps pipelines, CI/CD for ML, and model monitoring/observability About Us Slalom is a fiercely human business and technology consulting company that leads with outcomes to bring more value, in all ways, always. From strategy through delivery, our agile teams across 52 offices in 12 countries partner with clients to co-create powerful customer experiences, modern ways of working, and meaningful impact. What sets us apart? We believe work should be challenging and fulfilling, not perfect, but possible. That’s why we prioritize purpose, flexibility, connection, and recognition, so our people can thrive and love what they do, most days. Compensation and Benefits Slalom prides itself on helping team members thrive in their work and life. As a result, Slalom is proud to invest in benefits that include meaningful time off and paid holidays, parental leave, 401(k) with a match, a range of choices for highly subsidized health, dental, & vision coverage, adoption and fertility assistance, and short/long-term disability. We also offer yearly $350 reimbursement account for any well-being-related expenses, as well as discounted home, auto, and pet insurance. Slalom is committed to fair and equitable compensation practices. For this role, we are hiring at the following levels and salary ranges: • All locations: • Senior Architect: $163,000-$200,000 • Architect: $133,000-$163,000 • Senior Engineer: $108,000-$132,000 In addition, individuals may be eligible for an annual discretionary bonus. Actual compensation will depend upon an individual’s skills, experience, qualifications, location, and other relevant factors. The pay range is subject to change and may be modified at any time. We will accept applicants until the position is filled. EEO and Accommodations Slalom is an equal opportunity employer and is committed to attracting, developing and retaining highly qualified talent who empower our innovative teams through unique perspectives and experiences. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veterans’ status, or any other characteristic protected by federal, state, or local laws. Slalom will also consider qualified applications with criminal histories, consistent with legal requirements. Slalom welcomes and encourages applications from individuals with disabilities. Reasonable accommodations are available for candidates during all aspects of the selection process. Please advise the talent acquisition team if you require accommodations during the interview process. Locations: Austin, TX; Chicago, IL; Columbus, OH; Dallas, TX; Detroit, MI; Houston, TX; Indianapolis, IN; Kansas City, MO; Minneapolis, MN; Nashville, TN; St. Louis, MO

Requirements

Must-haves

Who You’ll Work With
We offer a flexible working environment to balance the need to work independently, with some days that may require in-person collaboration at our office.
What You’ll Do
Provide thought leadership on AI/ML, Generative AI, and Agentic AI internally and with clients, while contributing to a culture of collaboration, learning, and curiosity
Design end-to-end agentic AI architectures including planning loops, memory management, tool integration, and agent coordination patterns
Architect multi-agent orchestration systems using frameworks such asStrands Agents SDK,OpenAI Agents SDK, Google ADK, Lang Graph or similar for autonomous reasoning, decision-making, and task execution
Design and implement Model Context Protocol (MCP) server integrations for tool use, data access, and cross-system interoperability with enterprise systems
Build advanced retrieval-augmented generation (RAG) systems including vector databases, embedding strategies, chunking optimization, hybrid search, re-ranking, and multi-source data synthesis
Design and deliver AI and ML solutions across AWS, Azure, and GCP, using the right mix combination of cloud-native data services, ML tooling, LLM platforms, and software engineering practices
Build in Python and, where useful, other languages to deliver machine learning systems, APIs, evaluation harnesses, retrieval pipelines, agent workflows, and production services
Recommend and implement architecture for model and agent pipelines, CI/CD, testing, deployment, observability, andMLOps/LLMOps at scale
Implement evaluation frameworks (e.g., RAGAS, DeepEval, LangSmith) to measure task success rates, tool-call accuracy, and reasoning integrity for GenAI systems
Build guardrails for safety, compliance, and performance monitoring including human-in-the-loop (HITL) approval workflows, escalation policies, and sandbox isolation
Define AI governance frameworks including model risk management, responsible AI practices, regulatory compliance, and authorization boundaries for autonomous decision-making
Explain model and system behavior to both technical and non-technical audiences, including leading deep technical presentations, workshops, and architecture conversations
Collaborate with Product Owners to apply Slalom’s agile process and lead the initiation, delivery, and transition of projects in a client-facing role
Lead and mentor engineers and machine learning practitioners. Lead smaller projects (3 to 5 people) as the technical lead from project initiation to delivery
Build trusted relationships with customers and collaborate across Slalom teams to share learnings and strengthen the broader Intelligence Engineering practice
Will be delivery-focused approximately 85–95% of the time
Travel may be required on a limited basis dependent on your client engagement or internal meetings/events.
What You’ll Bring
Hands-on experience designing or building multi-agent systems including agent orchestration, tool integration, and autonomous decision-making workflows

Nice-to-haves

(Preferred) Experience with Model Context Protocol (MCP) server development and integration
(Preferred) Experience with MLOps/LLMOps pipelines, CI/CD for ML, and model monitoring/observability

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlazurellmawsgcppythonmachine learningci/cdagileapi

Platform: slalom
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Parental Leave