Description and RequirementsJob Description
Who You'll Work WithAs the Director of AI Systems and Platforms, you will manage a portfolio of client delivery and capability responsibilities while remaining an active technical leader. You will lead and develop AI/ML engineers, architects, and data scientists; shape and sell complex work; manage capability/practice health; and personally help teams design, build, and operate production AI systems. This role is for a leader who can move comfortably between client stakeholder conversations, architecture decisions, working sessions, code and design reviews, and production troubleshooting.
What You'll DoHands-On Client Delivery and Technical Leadership- Lead from the front on complex AI/ML engagements. Personally contribute to discovery, solution architecture, rapid prototypes, reference implementations, code and design reviews, performance tuning, and resolution of critical production issues.
- Architect, prototype, build, and productionize agentic AI, retrieval-augmented generation (RAG), predictive ML, optimization, and intelligent workflow solutions. Translate business outcomes into technical designs, delivery increments, acceptance criteria, and measurable production results.
- Develop and review production code using Python, SQL, PySpark, and API frameworks such as FastAPI and Pydantic. Apply software engineering practices including automated testing, version control, secure coding, dependency management, and repeatable build and release processes.
- Build with vendor-balanced AI platforms and model ecosystems, including Microsoft Foundry and Azure Machine Learning; Amazon Bedrock and Amazon SageMaker; Google Vertex AI and Gemini; and model or API ecosystems such as OpenAI, Anthropic, and Hugging Face.
- Apply agent and RAG frameworks such as LangGraph/LangChain, LlamaIndex, and Semantic Kernel, together with vector and search technologies such as Azure AI Search, OpenSearch, Pinecone, Weaviate, pgvector, or equivalent services.
- Design and deliver data and AI workloads on Databricks and Snowflake, using technologies such as Spark, Delta Lake, MLflow, feature stores, model registries, and governed data products.
- Engineer containerized and cloud-native services using Docker, Kubernetes, managed compute, serverless patterns, event-driven integration, REST APIs, and streaming or batch pipelines.
- Establish MLOps and LLMOps practices across CI/CD, infrastructure as code, model and prompt versioning, automated evaluation, observability, lineage, security, and cost management. Use tools such as GitHub Actions, Azure DevOps or GitLab CI, Terraform, OpenTelemetry, cloud-native monitoring, MLflow, and fit-for-purpose evaluation frameworks.
- Define and enforce production quality standards for groundedness, relevance, safety, latency, reliability, scalability, privacy, and responsible AI. Design human-in-the-loop controls, guardrails, auditability, and fallback patterns appropriate to each use case.
Engagement and Portfolio Leadership- Lead engagements from opportunity discovery and current-state assessment through architecture, delivery, production launch, and operational transition, ensuring alignment to business outcomes and technical excellence.
- Own solution and portfolio health across multiple workstreams or engagements, including scope, staffing, delivery approach, dependencies, financial performance, risks, quality, and stakeholder communication.
- Create clarity across client and Slalom teams by defining architecture decisions, roles, delivery milestones, technical quality gates, and escalation paths.
- Step directly into delivery when risk is high or ambiguity is blocking progress - facilitating technical workshops, validating designs, pairing with engineers, reviewing implementation choices, or leading incident and root-cause analysis.
- Partner with product, experience, data, security, legal, risk, and change leaders to ensure AI solutions are responsibly usable, governable, supportable, and adopted.
People and Capability/Practice Leadership- Hire, lead, mentor, and retain a high-performing, inclusive team of AI/ML engineers, architects, and data scientists. Set clear expectations, provide timely feedback, and create meaningful development and stretch opportunities.
- Guide other people leaders, strengthen the pipeline of future leaders, and ensure fair, objective, and accountable performance management across the team.
- Translate organizational strategy and change into clear priorities for the team, communicating transparently and leading with empathy.
- Inform team health by planning capacity against demand and managing hiring, skills, staffing, utilization, retention, and sustainable growth.
- Deliver against individual and team utilization, revenue contribution, pipeline, and operational goals while balancing client outcomes, team health, and long-term capability investment.
- Own development of reusable reference architectures, accelerators, delivery playbooks, engineering standards, and knowledge-management practices that improve quality and speed across the AISP capability.
- Create and manage learning opportunities through technical communities, hands-on labs, internal training, mentoring, and practical guidance grounded in current client delivery.
Client, Market, and Growth Leadership- Serve as a trusted technical and business advisor to client executives, engineering leaders, data and AI leaders, and product teams. Connect AI investments to measurable business value, operating-model implications, and responsible adoption.
- Identify and shape opportunities in new and existing client environments. Lead technical discovery, solutioning, estimation, staffing, proposal development, and executive presentations for complex AI and ML pursuits.
- Partner across Slalom capabilities, markets, global teams, and alliance partners to assemble integrated solutions and the right delivery team.
- Use delivery insight and market signals to evolve offerings and go-to-market strategies across agentic AI, model engineering, AI platforms, and AI/ML operations.
- Represent Slalom through client workshops, technical briefings, published perspectives, industry events, and partner forums.
What You'll BringYou are a hands-on engineering expert and a business-minded capability/practice leader who can build credibility with executives and practitioners alike. You combine portfolio and people leadership with the technical depth to make consequential architecture decisions, challenge implementation choices, and help a team deliver when the work gets difficult.
- 10+ years of experience in AI, machine learning, data science, software engineering, or a closely related field, including substantial experience delivering production systems.
- 8+ years of software engineering experience building and operating production services, data products, ML platforms, or intelligent applications.
- 5+ years of technical leadership, people leadership, or capability management experience, including responsibility for hiring, coaching, performance, staffing, and team health.
- 2+ years of hands-on experience designing and delivering GenAI, LLM, RAG, or agentic AI systems beyond proof of concept and into controlled production use.
- Strong current coding ability in Python and SQL, with the judgment to create prototypes and reference implementations, review production code, diagnose failures, and guide engineering tradeoffs.
- Deep experience with RAG and agentic architectures, including embeddings, chunking and retrieval strategies, vector or hybrid search, reranking, context management, tool use, memory, orchestration, evaluation, and guardrails.
- Demonstrated experience with at least two major cloud/data AI ecosystems and the ability to select technologies based on client context rather than vendor preference.
- Experience designing production-grade MLOps/LLMOps capabilities, including CI/CD, model and prompt lifecycle management, infrastructure as code, automated evaluation, monitoring, observability, security, governance, and FinOps.
- Experience leading multi-workstream consulting engagements or delivery portfolios, with accountability for client outcomes, solution quality, financial and operational health, and senior stakeholder relationships.
- Demonstrated success shaping and selling complex technical work, developing estimates and staffing models, and contributing to revenue and utilization goals.
- Recognized expertise in one or more areas such as agentic AI, RAG systems, model engineering, AI platforms, MLOps/LLMOps, or responsible AI.
- Ability to lead through ambiguity, integrate diverse perspectives, resolve conflict, and drive alignment across technical, business, risk, and executive stakeholders.
- Excellent written, verbal, workshop facilitation, and executive communication skills.
