ROLE SUMMARY
As Manager – AI Engineering, you will lead a multidisciplinary team of AI Engineers and Data Scientists delivering GenAI-powered solutions, agentic AI systems, LLM-integrated applications, machine learning models, and retrieval-enabled intelligent products. This role combines technical leadership, people management, delivery accountability, and product ownership. You will be responsible for ensuring that the team delivers scalable, secure, supportable, and business-relevant AI and ML solutions from discovery through production operation and continuous improvement.
This is not a lightweight line-management role. The successful candidate must be technically strong enough to guide senior engineers, data scientists, and technical leads, make sound architectural and implementation trade-offs, and raise the engineering bar of the team. You are expected to provide hands-on technical direction across LLM-powered systems, agent orchestration, RAG, ML model development, MLOps, cloud-native application delivery, integrations, observability, and SDLC discipline, while also building a culture of accountability, ownership, experimentation rigor, and engineering excellence.
You will work closely with Product Managers, Solution Architects, Lead AI Engineers, Lead Data Scientists, Platform teams, DevOps, Security, and business stakeholders to translate business goals into implementable engineering work and durable product outcomes.
KEY RESPONSIBILITIES
- Lead and mentor a team of AI Engineers and Data Scientists across varying experience levels, including senior engineers and technical leads, ensuring strong delivery quality, modeling rigor, engineering discipline, and technical growth
- Own end-to-end delivery of GenAI, agentic AI, and ML-enabled solutions — from discovery, design, and backlog shaping through development, testing, deployment, monitoring, and continuous improvement
- Guide architectural and design decisions across LLM-powered systems, embeddings pipelines, retrieval-augmented generation (RAG), agent orchestration, tool-enabled workflows, ML model pipelines, APIs, and cloud-native application services
- Oversee the design, development, evaluation, and operationalization of machine learning models for predictive, classification, recommendation, anomaly detection, forecasting, optimization, or other business use cases where applicable
- Ensure strong practices across feature engineering, experimentation, validation, model performance assessment, explainability, drift awareness, and MLOps readiness
- Translate business and product priorities into executable engineering and data science plans, technical workstreams, model delivery milestones, and sustainable release outcomes
- Review and challenge implementation choices to ensure systems and models are scalable, secure, observable, cost-aware, and maintainable
- Partner with Product Managers and engineering leadership to prioritize work, manage technical dependencies, and align delivery with roadmap goals
- Establish strong SDLC and build-own-operate practices within the team, including design reviews, code reviews, model reviews, automated testing, release readiness, production support, reliability improvement, and technical debt management
- Drive reuse and productivity by scaling frameworks, shared components, prompt templates, orchestration patterns, feature templates, modeling utilities, evaluation frameworks, and internal accelerators across the team
- Promote a culture of responsible AI and operational excellence, emphasizing security, token and cost governance, model safety, quality, observability, reproducibility, and supportability
- Coordinate with cloud platform, DevOps, Security, Integration, Architecture, and data teams to ensure enterprise readiness of all deployments
- Own hiring, onboarding, coaching, performance development, and growth plans for both the AI Engineering and Data Science team members
- Track and communicate KPIs related to delivery, GenAI adoption, model quality, latency, business impact, reliability, experimentation outcomes, and production performance
- Act as the primary technical and delivery escalation point for the team, helping remove blockers and resolve design, modeling, execution, and operational issues
Required Qualifications
- 10+ years of experience in software engineering, AI / ML engineering, data science, solution engineering, or technology delivery, including strong experience building and operating production-grade intelligent systems
- 4 to 5 + years of experience delivering or leading AI / ML / GenAI / LLM-powered solutions in enterprise or product environments
- Proven experience leading multidisciplinary teams or technical pods delivering LLM-powered products, agentic AI workflows, machine learning models, or AI-enabled application capabilities, with accountability for both technical quality and delivery outcomes
- Strong technical depth in Python, modern backend engineering, machine learning solution delivery, API-first architectures, microservices, distributed systems, and cloud-native application delivery
- Strong hands-on or design-level experience with LLM platforms and orchestration frameworks such as Azure OpenAI, Azure AI Studio, Semantic Kernel, LangChain, AutoGen, or equivalent platforms used for enterprise GenAI delivery
- Strong experience designing or guiding implementations involving retrieval-augmented generation (RAG), embeddings pipelines, vector search, grounding strategies, and retrieval optimization using platforms such as Azure AI Search, Pinecone, Weaviate, FAISS, or equivalent
- Strong understanding of machine learning model development, including feature engineering, model training, validation, tuning, evaluation, performance interpretation, and production-readiness considerations
- Practical experience guiding or reviewing MLOps practices, including experiment tracking, model versioning, deployment automation, CI/CD for ML, monitoring, drift detection, retraining readiness, and reproducibility
- Experience building and deploying AI- and ML-enabled cloud-native services using technologies such as Azure Functions, Azure Container Apps, FastAPI, Docker, Azure DevOps, GitHub, GitHub Actions, Kubernetes / AKS, Azure Machine Learning, Databricks, MLflow, or equivalent engineering and deployment platforms
- Strong understanding of CI/CD, containerization, deployment automation, secure delivery practices, and operational readiness for AI-driven and ML-enabled systems
- Knowledge of Model Context Protocol (MCP), agent-to-agent (A2A) interaction models, memory / context management approaches, and other distributed AI coordination patterns
- Practical experience with observability and operational tooling such as Application Insights, Azure Monitor, OpenTelemetry, Log Analytics, Datadog, New Relic, or equivalent platforms, including monitoring of reliability, latency, cost, runtime behavior, and model / workflow health
- Strong understanding of agentic AI implementation patterns, including multi-step orchestration, tool calling, context management, and workflow decomposition
- Experience integrating AI- and ML-enabled solutions with REST APIs, enterprise systems, workflow platforms, event-driven services, or downstream business applications
- Demonstrated ability to translate business and product needs into scalable, secure, and maintainable AI / ML engineering solutions, while guiding teams on implementation trade-offs, experimentation choices, and delivery sequencing
- Strong SDLC ownership mindset across design, build, testing, deployment, support, reliability improvement, model lifecycle management, and long-term maintainability
- Proven ability to raise engineering and data science quality through code reviews, model reviews, design guidance, architectural mentoring, coaching of senior engineers and data scientists, and reinforcement of reusable patterns and standards
- Strong people leadership capability, including coaching, feedback, performance management, capability development, and fostering accountability and engineering excellence
- Strong collaboration and communication skills, with the ability to work effectively across engineering, data science, product, platform, architecture, DevOps, and business stakeholders
Preferred Qualifications
- Experience leading implementations involving agentic AI workflows, multi-agent coordination, tool-enabled automation, reusable orchestration abstractions, or structured task delegation patterns
- Experience with AI observability, prompt safety, runtime guardrails, hallucination mitigation, evaluation frameworks, quality monitoring, and enterprise governance practices for GenAI systems
- Familiarity with broader enterprise AI and ML platforms such as Microsoft AI Foundry, Azure Machine Learning, PromptFlow, MLflow, Databricks, or equivalent AI / ML lifecycle and experimentation ecosystems
- Experience leading or supporting teams working on forecasting, optimization, recommender systems, anomaly detection, classification, NLP, or hybrid ML + GenAI solutions
- Experience contributing to reusable GenAI accelerators, internal SDKs, orchestration templates, prompt frameworks, feature templates, evaluation patterns, modeling utilities, or shared engineering utilities
- Familiarity with enterprise integration landscapes involving SAP, ServiceNow, API management layers, workflow systems, event buses, and business process platforms
- Experience with cost-aware AI and ML delivery, including token usage visibility, model selection trade-offs, compute efficiency, scaling considerations, and engineering productivity optimization
- Ability to communicate complex technical and analytical decisions clearly to both engineers and non-technical stakeholders, and to represent team direction confidently in leadership forums
- Experience operating in a build-own-operate product environment
- Knowledge of responsible AI, AI quality engineering, governance-by-design, model risk awareness, and compliance-aware delivery in enterprise environments
