{"id":2688445,"url":"https://alion.io/job/mphasis-principal-ai-software-engineer","title":"Principal AI Software Engineer","company":{"id":59446,"name":"Mphasis","domain":"mphasis.com","url":"https://alion.io/company/mphasis","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"RippleHire","truth_index":{"grade":"B","score":75,"open_postings":15,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":28,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":71000,"max_usd":139000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":21},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google AI Studio","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"Google GKE","optional":false},{"name":"Hybrid Search","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false},{"name":"FAISS","optional":true},{"name":"GraphRAG","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"Kubernetes","optional":true},{"name":"MLFlow","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-10-15T00:00:00Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-10-18T10:16:26Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Job Title - AI Engineer\nWe are seeking a hands-on AI Engineer / Generative AI Developer to design, develop, test, and deploy enterprise AI applications on Google Cloud Platform (GCP). The ideal candidate will have practical experience with Gemini Enterprise, Vertex AI, Large Language Models, Retrieval-Augmented Generation, conversational AI, and cloud-native application development.\nThis role will focus on building AI assistants, enterprise search solutions, RAG applications, and agent-based workflows. The candidate will work closely with AI architects, data engineers, product owners, and business stakeholders to translate functional requirements into secure, scalable, and reliable AI solutions.\nYears of Experience:\n5+ years of overall experience in software development, cloud engineering, data engineering, or AI/ML.2+ years of hands-on experience developing AI/ML or Generative AI applications.Practical experience building applications using GCP, Vertex AI, and Gemini.Experience implementing RAG, semantic search, conversational AI, or enterprise knowledge-assistant solutions.Experience working within Agile software development and delivery teams.Technical Skills:\nRequired:\nGenerative AI and Gemini\nGemini models and APIsGemini EnterpriseVertex AI and Generative AI StudioPrompt engineering and prompt managementFunction calling and tool integrationConversational AI and AI assistantsAgentic AI concepts and workflowsModel evaluation, grounding, and response-quality testingRAG and Enterprise Knowledge Solutions\nRetrieval-Augmented GenerationDocument ingestion and preprocessing\nChunking, metadata enrichment, and indexing\nEmbeddings and vector search\nSemantic and hybrid search\nRetrieval, re-ranking, and grounded response generation\nStructured and unstructured enterprise data integration\nGoogle Cloud Platform\nVertex AI\nGemini Enterprise\nBigQuery\nCloud Storage\nCloud Run\nCloud Functions\nPub/Sub\nCloud SQL\nVertex AI Vector Search\nBasic familiarity with GKE and Dataflow\nAI and Application Development\nPython\nFastAPI\nREST APIs\nLangChain, LangGraph, or LlamaIndex\nJSON and SQL\nGit-based development\nUnit, integration, and API testing\nLogging, monitoring, and application troubleshooting\nDevOps and MLOps\nCI/CD for AI applicationsCloud Build, GitHub Actions, or equivalent toolingContainerization using DockerVertex AI Pipelines or comparable orchestration toolsModel and prompt versioningApplication monitoring and observabilityBasic Infrastructure as Code knowledge, preferably TerraformSecurity and Responsible AI\nSecure API and application-development practicesIdentity and access managementData privacy and protectionResponsible AI principlesPrompt-injection and sensitive-data handling controlsAI output monitoring and auditabilityPreferred Certifications:\nGoogle Cloud Associate Cloud EngineerGoogle Cloud Professional Machine Learning EngineerGoogle Cloud Generative AI certification or skill badgeGoogle Cloud Professional Cloud DeveloperRelevant Python, AI/ML, or cloud-development certificationKey Responsibilities:\nAI Application Development\nDevelop and enhance Generative AI applications using Gemini Enterprise, Gemini models, and Vertex AI.Build AI assistants, chatbots, enterprise search solutions, and agent-enabled workflows.Develop reusable Python components, APIs, connectors, and integration services.Integrate AI applications with enterprise systems, data sources, APIs, and business workflows.Convert business and functional requirements into working application features.RAG and Knowledge Solutions\nBuild document ingestion, parsing, chunking, embedding, and indexing pipelines.Implement semantic search, vector retrieval, re-ranking, and grounded response generation.Connect AI applications to structured and unstructured enterprise information sources.\nImprove retrieval relevance and response quality through testing and tuning.\nSupport document refresh, metadata management, access control, and knowledge lifecycle processes.\nTesting and Evaluation\nCreate test cases for prompts, retrieval results, model responses, and end-to-end workflows.\nEvaluate responses for relevance, groundedness, accuracy, completeness, and consistency.\nIdentify hallucinations, retrieval gaps, and response-quality issues.\nSupport regression testing when prompts, models, data sources, or application components change.\nDocument test results, known limitations, and remediation actions.\nCloud Deployment and Operations\nDeploy AI applications using GCP services such as Cloud Run, Cloud Functions, Vertex AI, and GKE.\nSupport CI/CD pipelines and automated application deployments.Monitor application performance, errors, latency, usage, and cloud consumption.Troubleshoot application, integration, data, and deployment issues.Contribute to performance, scalability, reliability, and cost improvements.Security and Governance\nFollow established enterprise architecture, security, data-governance, and Responsible AI standards.Implement authentication, authorization, data-protection, and application-logging requirements.Apply controls for sensitive information, unsafe prompts, model responses, and enterprise data access.Maintain appropriate technical documentation and evidence required for reviews and audits.Escalate security, privacy, model-risk, or data-quality concerns to the appropriate leads.Collaboration and Documentation\nWork with AI architects, senior engineers, data engineers, QA teams, product owners, and business analysts.Participate in technical design discussions, code reviews, sprint planning, demonstrations, and retrospectives.Prepare technical documentation, configuration guides, API specifications, and operational runbooks.Provide implementation updates, identify delivery risks, and support issue resolution.Share reusable components, development practices, and lessons learned with the engineering team.Preferred Qualifications\nExperience implementing enterprise AI assistants or knowledge-search applications using Gemini Enterprise.Experience integrating Gemini or Vertex AI with business applications and enterprise data sources.Experience with Vertex AI Vector Search, pgVector, Pinecone, ChromaDB, or FAISS.Familiarity with BigQuery, Pub/Sub, Cloud SQL, and GCP data services.Knowledge of GraphRAG, knowledge graphs, or multi-agent patterns is an advantage but not mandatory.Exposure to MLflow or other experiment and model-tracking tools.Experience working in a regulated or large-enterprise environment.Location: Toronto, ON\nWork Mode: Hybrid, 3-4 days per week\nAbout Mphasis\nMphasis applies next-generation technology to help enterprises transform businesses globally. Customer centricity is foundational to Mphasis and is reflected in the Mphasis’ Front2Back™ Transformation approach. Front2Back™ uses the exponential power of cloud and cognitive to provide hyper-personalized (C=X2C2TM=1) digital experience to clients and their end customers. Mphasis’ Service Transformation approach helps ‘shrink the core’ through the application of digital technologies across legacy environments within an enterprise, enabling businesses to stay ahead in a changing world. Mphasis’ core reference architectures and tools, speed and innovation with domain expertise and specialization are key to building strong relationships with marquee clients.\nEqual Opportunity Employer:\nMphasis is an equal opportunity/affirmative action employer. We provide equal employment opportunities to applicants and existing associates and evaluate qualified candidates without regard to race, gender, national origin, ancestry, age, color, religious creed, marital status, genetic information, sexual orientation, gender identity, gender expression, sex (including pregnancy, breast feeding and related medical conditions), mental or physical disability, medical conditions military and veteran status or any other status or condition protected by applicable federal, state, or local laws, governmental regulations and executive orders. View the EEO in the law poster , view the EEO in the law supplement . To view the pay transparency nondiscrimination provision please click and to view the E-Verify posting click .\nMphasis is committed to providing reasonable accommodations to individuals with disabilities. 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