Senior AI Engineer

Adroit Innovative Solutions Inc

Website: adroitinnovative.com
Job details:

• Google Cloud Platform
• Vertex AI
• Gemini Pro / Ultra
• BigQuery
• Dataflow /
• Pub/Sub
• Cloud Run / GKE
• LangChain / LlamaIndex
• RAG Pipelines
• Python
• TensorFlow / JAX
• Looker / Data Studio
• MLflow / Kubeflow
• Google Workspace APIs
• Vector Search (Vertex)
• Data Science
• GenAI APIs

CORE TECHNOLOGY SKILLS:

KEY RESPONSIBILITIES

1. Generative AI & LLM Development

• Design, build, and deploy generative AI applications using Google Gemini (Pro, Ultra, Flash), PaLM 2, and other Google-hosted foundation models via Vertex AI.
• Implement Retrieval-Augmented Generation (RAG) architectures using Vertex AI Search, Vector Search, and document embedding pipelines for enterprise knowledge retrieval.
• Develop multi-modal AI capabilities leveraging Gemini's vision, text, and code understanding for hospitality use cases such as guest experience, analytics, and operations.
• Build and maintain agentic AI workflows and orchestration using LangChain, LlamaIndex, or Google Agent Builder — integrating tools, APIs, and enterprise data sources.
• Optimize prompt engineering strategies, system instructions, and grounding mechanisms for production-grade LLM deployments.

2. Data Science & ML Engineering

• Develop end-to-end ML pipelines from data ingestion and feature engineering through model training, evaluation, and production deployment on Vertex AI Pipelines / Kubeflow.
• Apply advanced data science techniques — statistical modelling, time-series forecasting, recommendation systems, and anomaly detection — for hospitality and gaming analytics.
• Build scalable data transformation and feature engineering workflows using BigQuery, Dataflow, and Pub/Sub.
• Implement model monitoring, drift detection, and automated retraining strategies to ensure sustained model performance in production.
• Leverage TensorFlow, JAX, or PyTorch for custom model development where pre-trained solutions are insufficient.

3. GCP Platform & Cloud Architecture

• Architect and manage cloud-native AI infrastructure on GCP — including Vertex AI, BigQuery ML, Cloud Run, GKE, Cloud Functions, and Cloud Storage.
• Design secure, scalable, and cost-optimized GCP environments aligned with enterprise compliance requirements and CLIENT's data governance standards.
• Implement CI/CD pipelines for ML model serving using Cloud Build, Artifact Registry, and Vertex AI Model Registry.
• Set up monitoring, observability, and alerting for AI/ML workloads using Cloud Monitoring, Cloud Logging, and custom dashboards in Looker.
• Build agents with Google ADK, deploy with Cloud Run / Agent Engine with Vertex.
• Design and implement conversational AI agents using Dialogflow CX and Agent Builder for guest-facing and internal automation use cases.

4. Collaboration, Governance & Mentorship

• Partner with CLIENT's business and technology stakeholders to define AI use cases, prioritize the roadmap, and translate requirements into technical deliverables.
• Champion responsible AI practices — model fairness, explainability, content safety, and data privacy — across all AI solution designs.
• Produce and maintain technical documentation including architecture decision records (ADRs), API specs, model cards, and runbooks.
• Mentor junior engineers and lead knowledge-sharing sessions; contribute to AI community of practice within the delivery organization.

LEVEL-SPECIFIC EXPECTATIONS

L2 — Senior AI Engineer (5–8 Years)

Independently deliver well-scoped AI features and integrations on GCP with minimal supervision.

• Implement and optimize ML models, RAG pipelines, and LLM integrations under architectural guidance from L3/L4 peers.
• Write production-quality Python code with strong test coverage; participate actively in code reviews.
• Collaborate with data engineers and product managers to scope, estimate, and deliver sprint-level AI deliverables.
• Demonstrate growing ownership of GCP service configuration and cost management for assigned workloads.
• Build agents with Google ADK, deploy with Cloud Run / Agent Engine with Vertex.

L3 — Senior AI Engineer (8–10 Years)

• Lead end-to-end design and delivery of AI modules — from architecture to production deployment — for complex, multi-component features.
• Define LLM integration patterns, RAG strategies, and data pipeline architectures; own technical quality and performance of these systems.
• Act as the primary technical interface with cross-functional stakeholders at CLIENT; participate in requirements workshops and solution demos.
• Drive design reviews, establish engineering standards, and actively mentor L2 engineers on the team.
• Identify and address technical debt, reliability risks, and scalability bottlenecks proactively.
• Build agents with Google ADK, deploy with Cloud Run / Agent Engine with Vertex.

L4 — Lead / Principal AI Engineer (10+ Years)

• Own the end-to-end AI architecture for the CLIENT engagement — defining technology choices, platform strategy, and delivery approach across workstreams.
• Lead cross-team technical alignment and serve as the escalation point for complex engineering decisions and production issues.
• Drive AI roadmap discussions directly with CLIENT's senior leadership and technology executives; translate strategic intent into execution plans.
• Oversee as well as participate in building agents with Google ADK, deploy with Cloud Run / Agent Engine with Vertex.
• Establish AI engineering best practices, governance frameworks, and reusable platform components that accelerate delivery across the broader program.
• Represent the organization in client-facing architecture reviews, RFPs, and strategic planning sessions; contribute to pre-sales and solutioning where needed.

QUALIFICATIONS & EDUCATION

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related technical field.
• L2: 5–8 years of hands-on engineering experience with at least 3+ years focused on AI/ML or LLM development on cloud platforms.
• L3: 6–10 years of experience with 5+ years in AI/ML engineering; prior experience leading technical workstreams in enterprise settings.
• L4: 10+ years with 8+ years in senior AI/ML roles; demonstrated track record of enterprise architecture ownership and client stakeholder management.
• Strong proficiency in Python; working knowledge of SQL; familiarity with infrastructure-as-code tools (Terraform, Cloud Deployment Manager) preferred.
• Google Cloud Professional certifications (Cloud Architect, ML Engineer, Data Engineer) are a strong differentiator at all levels.
• Excellent written and verbal communication skills; ability to present technical concepts to non-technical executive audiences.

GOOD-TO-HAVE SKILLS

• Experience with hospitality, gaming, or retail domains — understanding CLIENT's operational context is a significant advantage.
• Familiarity with additional Vector DB platforms such as Pinecone, Weaviate, pgvector, or Chroma alongside Vertex AI Vector Search.
• Knowledge of RPA tools or workflow automation platforms (UiPath, Power Automate) that complement AI pipelines.
• Exposure to responsible AI toolkits, model interpretability frameworks (SHAP, LIME), and AI governance practices.
• Experience with real-time inference architectures, streaming ML, and event-driven AI using Pub/Sub and Eventarc.

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