Network Engineer

Job Summary

Required Qualifications

• •Strong Python / .NET software engineering
• •Generative AI engineering
• •AWS-native cloud architecture
• •Enterprise system integration
• •Observability
• •Application security
• •Technical leadership across the engineering lifecycle
• •Solution design
• •Experimentation
• •Implementation
• •Production deployment
• •Monitoring
• •Continuous improvement
• •Translate business and product requirements into appropriate technical designs, implementation plans, and engineering tasks
• •Lead technical design reviews
• •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
• •Mentor junior and mid-level engineers through code reviews, design discussions, pair programming, and knowledge-sharing sessions
• •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
• •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
• •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
• •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
• •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
• •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
• •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 repos
• •Must be available for weekend shifts
• •Valid driver's license