Description
About Alvarez & Marsal
Alvarez & Marsal (A&M) is a global consulting firm with over 10,000 entrepreneurial, action and results-oriented professionals in over 40 countries. We take a hands-on approach to solving our clients' problems and assisting them in reaching their potential. Our culture celebrates independent thinkers and doers who positively impact our clients and shape our industry. The collaborative environment and engaging work—guided by A&M's core values of Integrity, Quality, Objectivity, Fun, Personal Reward, and Inclusive Diversity - are why our people love working at A&M.
The Team
Our DTS team covers the full breadth of Technology Consulting and M&A services, including -
- Technology M&A and Strategy - Assist clients to manage the technology aspects and business enablement of complex M&A, integrations and carve-outs as well as post-deal value creation
- Technology Consulting – End to end technology advisory for clients, including developing technology roadmaps, platform/cloud/data advisory as well as transformation excellence for a digital transformation
- Data & AI services - Helping clients in harnessing the power of data and cutting-edge analytics to drive intelligent decision-making and transform businesses.
- Develop GenAI and Agentic AI solutions that create real business value for clients through process re-invention
How you will contribute
The Lead / Senior Backend Engineer is a critical role responsible for designing, building, and operating production-grade backend systems that power enterprise AI, Machine Learning, and Generative AI solutions. This role sits at the intersection of AI orchestration, backend engineering, and enterprise system integration, ensuring that AI capabilities are delivered through reliable, scalable, secure, and observable services that integrate seamlessly with business workflows and platforms. This role exists to prevent failures by enforcing strong backend engineering discipline, resiliency, and operational excellence.
Key Responsibilities
1. Backend Architecture & Service Design
- Define and own backend architecture standards for AI-enabled platforms, including microservices, APIs, and orchestration layers
- Design backend systems that support AI-first workloads, including synchronous, asynchronous, and event-driven execution patterns
- Establish clear service boundaries, contracts, and ownership across backend components
- Partner with GenAI & Data Solution Architects to align backend architecture with overall AI platform strategy
- Ensure designs meet enterprise non-functional requirements for performance, scalability, availability, security, and cost
- Design, develop, and maintain scalable backend services and APIs using Python (FastAPI, Flask, Django)
- Maintain documentation and enforce best practices for coding, testing, and deployment
2. AI Orchestration & Workflow Management
- Own and implement AI orchestration services responsible for routing requests across models, tools, and services
- Coordinate multi-step AI workflows, agent execution, context management, and intermediate state handling
- Implement orchestration patterns for RAG pipelines, agentic workflows, tool calling, and long-running AI processes
- Ensure orchestration logic is modular, extensible, reusable, and production-ready
- Integrate LLM APIs and frameworks (e.g., OpenAI, LangChain) into enterprise applications
3. API Design & Enterprise Integration
- Design and enforce API contracts (REST, gRPC, async APIs) with versioning, backward compatibility, and deprecation strategies
- Integrate backend services with LLM providers, vector databases, embedding services, and enterprise systems (ERP, CMMS, CRM, EAM)
- Implement secure and performant integrations using authentication, authorization, throttling, and rate limiting
4. Resiliency, Reliability & Observability
- Implement resiliency patterns including retries with backoff, circuit breakers, timeouts, and graceful degradation
- Design systems to handle partial failures without cascading impact
- Implement comprehensive observability using structured logging, metrics, health checks, and distributed tracing
- Partner with DevOps and Platform teams to ensure production readiness, stability, and high availability
- Implement monitoring and alerting using Azure Monitor, Application Insights, or equivalent tools
5. AI Pipelines, Microservices & Cloud Operations
- Build and maintain backend microservices supporting AI pipelines and inference workflow
- Implement asynchronous processing for long-running AI tasks using queues, events, and workflow engines
- Design and implement CI/CD pipelines for automated build, test, and deployment
- Deploy and operate backend services on Azure using App Services, Functions, storage, and SQL/NoSQL services
- Optimize backend performance for latency, throughput, horizontal scaling, and cost efficiency
6. Security, Governance & Compliance
- Implement secure backend patterns including RBAC, secrets management, and secure API gateways
- Ensure compliance with enterprise security, privacy, and governance standards
- Enable auditability and traceability of AI requests and decisions in collaboration with governance teams
7. Collaboration, Mentorship & Delivery
- Collaborate with GenAI & Data Architects, AI Engineers, Data Scientists, Frontend Engineers, DevOps, QA, and Delivery teams
- Participate in Agile ceremonies including sprint planning, backlog refinement, and reviews
- Provide technical leadership, mentoring, and code/design reviews
- Promote engineering best practices and reusable platform components
Qualifications
- 7–10 years of experience in backend engineering building enterprise-grade applications and services
- Proven experience designing, deploying, and operating production systems at scale
- Experience supporting AI, ML, or data-intensive workloads preferred
- Strong proficiency in one or more backend languages: Python, Java, or C#/.NET
- Experience with backend frameworks such as FastAPI, Flask, Django, Spring Boot, or .NET Core
- Strong understanding of microservices architecture, API design, and asynchronous/event-driven systems
- Experience with AI integration patterns including LLMs, vector databases, and embeddings
- Strong experience with cloud-native backend development on Azure (AWS/GCP exposure acceptable)
- Experience building CI/CD pipelines and automating deployments
- Experience with relational and NoSQL databases (PostgreSQL, MySQL, MongoDB, Cosmos DB)
- Experience with background processing (Celery) and message brokers (Azure Service Bus, Kafka)
- Knowledge of security best practices, authentication/authorization, and data privacy
- Exposure to Docker and Kubernetes is a plus
- Familiarity with data processing libraries (Pandas, NumPy) is a plus
- Strong system design and problem-solving skills
- Ownership mindset focused on reliability and long-term maintainability
- Ability to clearly communicate technical designs and trade-offs
- Collaborative approach with a passion for building scalable, reusable platforms
Your journey at A&M
We recognize that our people are the driving force behind our success, which is why we prioritize an employee experience that fosters each person’s unique professional and personal development. Our robust performance development process promotes continuous learning, rewards your contributions, and fosters a culture of meritocracy. With top-notch training and on-the-job learning opportunities, you can acquire new skills and advance your career. We prioritize your well-being, providing benefits and resources to support you on your personal journey. Our people consistently highlight the growth opportunities, our unique, entrepreneurial culture, and the fun we have together as their favorite aspects of working at A&M. The possibilities are endless for high-performing and passionate professionals.
