Network Engineer

Network Engineer

Req number:

R8164

Employment type:

Full time

Worksite flexibility:

Hybrid

Who we are

CAI is a global services firm with over 9,000 associates worldwide and a yearly revenue of $1.3 billion+. We have over 40 years of excellence in uniting talent and technology to power the possible for our clients, colleagues, and communities. As a privately held company, we have the freedom and focus to do what is right—whatever it takes. Our tailor-made solutions create lasting results across the public and commercial sectors, and we are trailblazers in bringing neurodiversity to the enterprise.

Job Summary

The Senior AI Engineer is to design, build, integrate, and operate secure, scalable, and production-ready AI solutions within an enterprise environment.
The role combines strong Python / .NET software engineering with generative AI engineering, AWS-native cloud architecture, enterprise system integration, observability, and application security. The Senior AI Engineer will provide technical leadership across the engineering lifecycle, from solution design and experimentation through implementation, production deployment, monitoring, and continuous improvement.

Job Description

TERMS OF REFERENCE Position Title/ Designation: Senior AI Engineer Location: International Sourcing Contract Type: IDIQ Staff Augmentation Engagement Duration: One (1) year, subject for extension Reporting To: Ref#:IS-0948-0726 Position group: Applications Development Division: ITOP Project: AI and Big Data (AIBD) Category: Senior Sourcing: International About the Asian Development Bank Asian Development Bank (ADB) is an international development finance institution headquartered in Manila, Philippines and is composed of 69 members, 49 of which are from the Asia and Pacific region. ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. ADB combines finance, knowledge, and partnerships to fulfill its expanded vision under our Strategy 2030. ADB only sources from its 69 members. To view ADB Organizational Chart, please click here. ADB wants to ensure that everyone is treated with respect and given equal opportunities in an inclusive environment. ADB encourages the IDIQ agencies to submit all qualified candidates for the Agency Personnel Services requested regardless of their racial, ethnic, religious and cultural background, gender, sexual orientation or disabilities. About the Role: The Senior AI Engineer is to design, build, integrate, and operate secure, scalable, and production-ready AI solutions within an enterprise environment. The role combines strong Python / .NET software engineering with generative AI engineering, AWS-native cloud architecture, enterprise system integration, observability, and application security. The Senior AI Engineer will provide technical leadership across the engineering lifecycle, from solution design and experimentation through implementation, production deployment, monitoring, and continuous improvement. The successful candidate will work closely with AI engineers, data scientists, software engineers, cloud infrastructure teams, cybersecurity specialists, enterprise architects, product managers, and delivery partners to ensure that AI solutions are reliable, maintainable, secure, and aligned with organizational standards. Scope of Work/Responsibilities INTERNAL. This information is accessible to ADB Management and Staff. It may be shared outside ADB with appropriate permission. Technical Leadership • Translate business and product requirements into appropriate technical designs, implementation plans, and engineering tasks. • Lead technical design reviews and contribute to architecture, security, data, and operational readiness assessments. • Evaluate technical options and provide recommendations based on feasibility, scalability, security, performance, cost, and maintainability. • Identify technical dependencies, delivery risks, resource requirements, and architectural constraints early in the development lifecycle. • Guide engineers in resolving complex technical issues involving AI models, application services, data pipelines, cloud infrastructure, and enterprise integrations. • Support technical estimation, work planning, backlog refinement, and delivery prioritization. • Promote the use of shared enterprise AI capabilities and reusable platform services rather than duplicating solution-specific implementations. • Mentor junior and mid-level engineers through code reviews, design discussions, pair programming, and knowledge-sharing sessions. Backend Software Engineering • Design and develop high-quality Python and/or .NET services, libraries, APIs, background workers, and data-processing components. • Build modular, reusable, testable, and maintainable application components using established Python and/or .NET engineering practices. • Develop synchronous and asynchronous services that support AI inference, document processing, data retrieval, workflow orchestration, and system integration. • Implement appropriate exception handling, retry mechanisms, timeouts, circuit breakers, caching, rate limiting, and graceful degradation. • Apply object-oriented, functional, domain-driven, and event-driven design approaches where appropriate. • Develop automated unit, integration, contract, security, performance, and regression tests. • Maintain clear technical documentation covering solution architecture, APIs, configuration, deployment, operations, and troubleshooting. • Contribute to continuous integration and continuous delivery pipelines for automated testing, security scanning, deployment, and release management. • Participate in code reviews and ensure that engineering work meets agreed quality, security, performance, and maintainability standards. System Integrations • Design and implement secure integrations between AI solutions and enterprise applications, data platforms, document repositories, workflow systems, and external services. • Develop and maintain REST, event-driven, messaging, streaming, batch, and file-based integration patterns. • Build integrations using APIs, webhooks, message queues, event buses, managed file transfer, and other approved enterprise integration mechanisms. • Implement authentication and authorization using enterprise identity standards such as OAuth 2.0, OpenID Connect, service identities, API credentials, and role-based access controls. • Integrate AI solutions with structured and unstructured data sources while preserving source permissions, data classifications, and access-control requirements. • Develop connectors for enterprise systems such as document management platforms, service management tools, data warehouses, databases, search platforms, and business applications. • Define API contracts, data schemas, error-handling conventions, versioning strategies, and integration testing requirements. • Coordinate with application owners and platform teams to resolve integration constraints, access requirements, service limits, and dependency timelines. • Ensure that integrations are observable, resilient, idempotent where necessary, and designed to handle partial failures safely. AWS-Native Cloud Engineering • Design and implement AI solutions using approved AWS-native cloud services and architectural patterns. • Develop solutions using relevant services such as Azure OpenAI, Amazon Bedrock, AWS Lambda, Amazon ECS, Amazon EKS, Amazon API Gateway, Amazon S3, Amazon RDS, Amazon OpenSearch Service, Amazon EventBridge, Amazon SQS, Amazon SNS, AWS Step Functions, and AWS Secrets Manager. • Implement cloud-native patterns for serverless processing, containerized workloads, event-driven architecture, workflow orchestration, batch processing, and API-based services. INTERNAL. This information is accessible to ADB Management and Staff. It may be shared outside ADB with appropriate permission. • Design solutions that meet enterprise requirements for availability, scalability, resilience, performance, backup, disaster recovery, and cost management. • Work with cloud infrastructure teams to define network connectivity, private endpoints, security groups, encryption, logging, and environment configurations. • Contribute to infrastructure-as-code implementations using Terraform. • Optimize cloud resource usage, model consumption, storage, data transfer, and compute costs. • Support deployments across development, testing, staging, and production environments. • Troubleshoot application, platform, network, permissions, capacity, and service-integration issues within AWS environments. Generative AI Engineering • Design and build generative AI solutions using foundation models, large language models, embedding models, reranking models, and multimodal capabilities. • Develop retrieval-augmented generation applications that combine enterprise content, search services, vector retrieval, metadata filtering, and generative models. • Design prompt templates, system instructions, tool descriptions, response schemas, and conversation flows. • Implement model routing, fallback, retry, timeout, caching, and rate-limiting mechanisms. • Build AI agent and workflow capabilities that can select approved tools, retrieve information, invoke enterprise services, and complete controlled multi-step tasks. • Develop document ingestion, parsing, chunking, metadata enrichment, embedding, indexing, retrieval, and citation-generation pipelines. • Implement hybrid search approaches that may combine keyword search, semantic search, vector search, taxonomy, graph-based retrieval, and reranking. • Work with Data Scientists and AI Engineers to evaluate models and AI responses using measurable criteria such as relevance, groundedness, accuracy, completeness, safety, latency, and cost. • Develop automated and human-in-the-loop evaluation processes for prompts, models, retrieval strategies, and generated outputs. • Implement safeguards against prompt injection, insecure tool invocation, sensitive-data exposure, hallucination, inappropriate content, and unauthorized information retrieval. • Support AI red-teaming, adversarial testing, security testing, and responsible AI assessments. • Monitor model behaviour, token consumption, response quality, retrieval performance, latency, failures, and operational cost. • Document model limitations, solution assumptions, evaluation results, human oversight requirements, and appropriate-use conditions. Team Collaboration • Collaborate with product managers and business stakeholders to clarify requirements, intended outcomes, user expectations, and acceptance criteria. • Work with solution and enterprise architects to ensure alignment with organizational architecture principles and technology standards. • Partner with data engineers and data owners to address data quality, availability, lineage, classification, access, licensing, and refresh requirements. • Coordinate with infrastructure, platform engineering, and site reliability teams to establish production environments and operational support models. • Work closely with cybersecurity, data security, risk, legal, compliance, and responsible AI teams to implement required controls. • Participate in agile ceremonies, technical workshops, architecture reviews, security reviews, and operational readiness assessments. • Communicate technical risks, dependencies, design decisions, implementation trade-offs, and delivery progress clearly to technical and non-technical stakeholders. • Support delivery partners and vendors by defining technical expectations, reviewing deliverables, and ensuring alignment with enterprise engineering standards. • Contribute to engineering communities of practice, reusable technical assets, reference implementations, and internal knowledge repositories. • Foster a collaborative engineering culture that encourages constructive challenge, early escalation, shared ownership, and continuous improvement. Observability and Datadog • Implement application, infrastructure, model, retrieval, integration, and user-experience observability using Datadog. • Configure structured logging, metrics, distributed tracing, dashboards, alerts, monitors, and service-level indicators. • Instrument Python services to provide visibility into request processing, dependencies, database activity, external API calls, model invocations, and background jobs. INTERNAL. This information is accessible to ADB Management and Staff. It may be shared outside ADB with appropriate permission. • Establish correlation across application logs, traces, infrastructure telemetry, model requests, and business transactions. • Monitor AI-specific operational metrics such as model latency, token usage, inference failures, retrieval quality, empty results, fallback rates, and cost. • Define meaningful alerts that support early detection while minimizing unnecessary operational noise. • Support incident investigation, root-cause analysis, performance tuning, and post-incident improvement activities. • Ensure that logs and telemetry are designed to avoid exposing credentials, confidential data, personal information, prompts, or model responses without appropriate controls. • Develop operational dashboards for engineering teams, service owners, product managers, and support functions. • Contribute to service-level objectives, availability targets, performance baselines, and capacity planning. Software Engineering Security • Embed secure software development practices throughout the design, development, testing, deployment, and operation of AI solutions. • Use Wiz to identify and address cloud security risks, exposed resources, configuration weaknesses, excessive permissions, vulnerable workloads, and security posture issues. • Use Snyk to identify and remediate vulnerabilities in open-source dependencies, application code, container images, and infrastructure-as-code. • Integrate security scanning and policy checks into continuous integration and continuous delivery pipelines. • Review and remediate software composition analysis, static application security testing, container scanning, infrastructure-as-code scanning, and cloud posture findings. • Apply secure coding practices to prevent common vulnerabilities involving injection, insecure deserialization, authentication, authorization, sensitive-data exposure, and server-side request forgery. • Securely manage application secrets, API keys, certificates, database credentials, and model-provider credentials. • Implement appropriate encryption, access controls, audit logging, input validation, output filtering, and data-handling controls. • Assess third-party Python packages, AI frameworks, models, containers, and external services before adoption. • Work with cybersecurity teams to prioritize findings based on exploitability, business impact, data sensitivity, and production exposure. • Track security findings, exceptions, compensating controls, and remediation actions through till closure. • Support threat modelling and security assessments for AI applications, model integrations, retrieval pipelines, and agentic workflows. Production Readiness and Support • Ensure that AI services are production-ready, supportable, observable, secure, and resilient before release. • Contribute to deployment plans, rollback procedures, operational runbooks, support documentation, and post-deployment validation. • Support the preparation of technical evidence required for architecture, cybersecurity, operational risk, and Change Approval Board reviews. • Participate in production deployments and provide technical support during agreed implementation and stabilization periods. • Investigate production incidents and implement corrective and preventive improvements. • Manage technical debt and ensure that deferred engineering or security actions are recorded, prioritized, and remediated. • Support capacity planning, performance testing, resilience testing, backup validation, and disaster recovery exercises. • Continuously improve reliability, performance, security, developer productivity, and operational efficiency. Measures of Success Success in the role will be demonstrated through: • Reliable and maintainable Python AI services delivered in accordance with agreed engineering standards. • Secure, scalable, and observable AI solutions operating successfully in AWS environments. • Effective integration with enterprise applications, data sources, identity services, and shared platforms. • Early identification and resolution of technical, security, data, and operational risks. • Measurable improvements in AI response quality, retrieval performance, service reliability, and operational cost. • Timely remediation of security findings identified through Wiz, Snyk, and related engineering security controls. • Reduced production incidents through effective testing, observability, resilience engineering, and operational readiness. • Increased reuse of shared AI components, libraries, integration patterns, and platform capabilities. • Strong engineering collaboration, knowledge transfer, and development of less-experienced team members. • Successful transition of AI capabilities from experimentation into governed, secure, and supportable enterprise services. INTERNAL. This information is accessible to ADB Management and Staff. It may be shared outside ADB with appropriate permission. Requirement and Qualification (Education & Work Experience) Required Qualifications and Experience • Significant professional experience developing production-grade applications using Python. • Demonstrated experience designing and delivering enterprise AI, machine learning, data, or cloud-native solutions. • Strong understanding of Python and/or .NET frameworks and libraries used for APIs, asynchronous processing, data engineering, testing, and AI development. • Practical experience developing generative AI, retrieval-augmented generation, intelligent search, or AI agent solutions. • Hands-on experience with AWS-native cloud services and cloud-native architectural patterns. • Experience designing and integrating REST APIs, event-driven services, databases, enterprise applications, and data platforms. • Strong understanding of software architecture, distributed systems, microservices, application security, testing, and continuous delivery. • Experience implementing observability using Datadog or a comparable enterprise observability platform. • Experience using cloud and application security tools such as Wiz and Snyk. • Experience with container technologies and orchestration platforms such as Docker, Amazon ECS, or Amazon EKS. • Experience with automated testing, source control, code review, dependency management, and CI/CD pipelines. • Strong analytical, troubleshooting, technical writing, and stakeholder communication skills. • Demonstrated ability to lead technical discussions, mentor engineers, and influence engineering decisions without relying solely on formal authority. Preferred Qualifications and Experience • Experience delivering AI solutions in a regulated, financial, public-sector, or risk-sensitive enterprise environment. • Experience with Amazon Bedrock, vector databases, search platforms, model gateways, and AI evaluation frameworks. • Familiarity with responsible AI principles, model risk, bias assessment, explainability, human oversight, and AI governance. • Experience implementing model routing, AI guardrails, prompt security, retrieval security, and agent tool controls. • Familiarity with infrastructure as code, DevSecOps, MLOps, LLMOps, platform engineering, and site reliability engineering. • Experience with enterprise identity platforms, private cloud connectivity, API management, and role-based access control. • Relevant certifications or formal education in software engineering, cloud architecture, cybersecurity, data engineering, artificial intelligence, or a related discipline. Core Competencies • Advanced Python and/or .NET engineering. • Generative AI and retrieval engineering. • AWS cloud architecture. • Enterprise system integration. • Secure software development. • Technical leadership and mentoring. • Observability and operational troubleshooting. • Architecture and design thinking. • Structured problem-solving. • Technical risk management. • Stakeholder communication. • Team collaboration. • Continuous learning and improvement. Soft Skills: • Strong problem-solving abilities and attention to detail. • Strong communication skills to articulate technical dependencies, delivery risks, resource requirements, and architectural constraints to product managers and technical delivery managers. Education: • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. Reporting and Coordination • For the provision of staff augmentation services, the Agency Personnel will take instructions from the Manager at the Information Technology Department at ADB and work closely with the project team, internal and external stakeholders. • The Agency Personnel will provide any other services as required by the team and ADB ITD supervisor or user unit. INTERNAL. This information is accessible to ADB Management and Staff. It may be shared outside ADB with appropriate permission. Work Arrangement • Hybrid, requiring employees to report onsite three times a week. If the user unit requires contractors to report to HQ, compliance is mandatory. • Work Schedule is from 8:00AM – 5:00PM Manila Time.

Reasonable accommodation statement

If you require a reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employment selection process, please direct your inquiries to application.accommodations@cai.io or (888) 824 – 8111.