Principal AI Software Engineer

Job Title – AI Engineer We 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.

This 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. Years of Experience: 5+ 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: Required: Generative AI and Gemini Gemini models and APIs Gemini Enterprise Vertex AI and Generative AI Studio Prompt engineering and prompt management Function calling and tool integration Conversational AI and AI assistants Agentic AI concepts and workflows Model evaluation, grounding, and response-quality testing RAG and Enterprise Knowledge Solutions Retrieval-Augmented Generation Document ingestion and preprocessing Chunking, metadata enrichment, and indexing Embeddings and vector search Semantic and hybrid search Retrieval, re-ranking, and grounded response generation Structured and unstructured enterprise data integration Google Cloud Platform Vertex AI Gemini Enterprise Big.

Query Cloud Storage Cloud Run Cloud Functions Pub/Sub Cloud SQL Vertex AI Vector Search Basic familiarity with GKE and Dataflow AI and Application Development Python FastAPI REST APIs Lang. Chain, Lang. Graph, or Llama. Index JSON and SQL Git-based development Unit, integration, and API testing Logging, monitoring, and application troubleshooting DevOps and MLOps CI/CD for AI applications Cloud Build, GitHub Actions, or equivalent tooling Containerization using Docker Vertex AI Pipelines or comparable orchestration tools Model and prompt versioning Application monitoring and observability Basic Infrastructure as Code knowledge, preferably Terraform Security and Responsible AI Secure API and application-development practices Identity and access management Data privacy and protection Responsible AI principles Prompt-injection and sensitive-data handling controls AI output monitoring and auditability Preferred Certifications: Google Cloud Associate Cloud Engineer Google Cloud Professional Machine Learning Engineer Google Cloud Generative AI certification or skill badge Google Cloud Professional Cloud Developer Relevant Python, AI/ML, or cloud-development certification Key Responsibilities: AI Application Development Develop 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 Build 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. Improve retrieval relevance and response quality through testing and tuning.

Support document refresh, metadata management, access control, and knowledge lifecycle processes. Testing and Evaluation Create test cases for prompts, retrieval results, model responses, and end-to-end workflows. Evaluate responses for relevance, groundedness, accuracy, completeness, and consistency. Identify hallucinations, retrieval gaps, and response-quality issues.

Support regression testing when prompts, models, data sources, or application components change. Document test results, known limitations, and remediation actions. Cloud Deployment and Operations Deploy AI applications using GCP services such as Cloud Run, Cloud Functions, Vertex AI, and GKE. Support 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 Follow 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 Work 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

Experience 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, pg. Vector, Pinecone, ChromaDB, or FAISS. Familiarity with Big. Query, 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 Work Mode: Hybrid, 3-4 days per week About Mphasis Mphasis 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. Equal Opportunity Employer: Mphasis 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.

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