Senior AI Engineer – GenAI & Agentic AI Platform
At a glance
Senior ML Platform & Ops role at Axtria. Tokyo · full-time.
Pay not stated AI Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and launch RAG workflows, AI assistants and other LLM solutions on the platform each client uses
- Create agents that coordinate tasks, use tools, retain context and plan across multiple steps
- Run structured and unstructured pharma data through Databricks medallion layers and Informatica IDMC pipelines
- Set up semantic search and vector retrieval to give intelligent agents access to relevant information
- Lead Japanese client workshops, demonstrations and delivery reviews, while applying governance and safety controls
What you bring
- Six to eight years in AI/ML, data engineering or applied AI, including two years with production GenAI or LLM systems
- Hands-on work with at least two named platforms, plus working knowledge of all three
- Experience building multi-agent systems with orchestration, tool use, memory and planning
- Strong Databricks and medallion architecture skills, along with practical Informatica IDMC experience
- Python proficiency and experience with AI orchestration frameworks such as LangChain or LangGraph
Who this fits
This senior role suits an engineer who can work across AI applications, data platforms and integration, and engage pharma clients in Japanese. The position is hybrid in Tokyo, with three days onsite.
From the employer
ABOUT THE ROLE
Axtria is seeking a technically deep AI Engineer to design and deliver production-grade GenAI and Agentic AI solutions across its pharma and life sciences client portfolio. You will work across the full data and AI stack — medallion architecture, structured and unstructured data pipelines, Informatica IDMC-based integration, and intelligent agent systems — deploying across client environments that span Microsoft, OpenAI, and AWS ecosystems. Platform expertise is client-dependent; candidates are expected to be proficient across all three.
CLIENT-DEPENDENT AI PLATFORMS
Engagements vary by client infrastructure. Candidates must be capable across all three platforms.
Location: Tokyo - Hybrid (3 days onsite)
Microsoft Copilot Studio
Copilot build & publish · Custom connectors · Power Platform · Teams deployment · Enterprise governance
ChatGPT Enterprise
Enterprise rollout · OpenAI API · GPT-4o · Fine-tuning · Prompt governance · Data privacy controls
AWS Bedrock
Foundation model APIs · Agents for Bedrock · Knowledge bases · RAG on S3 · Lambda · Guardrails
CORE TECHNICAL PILLARS
GenAI
LLM integration · RAG · Fine-tuning · Prompt engineering · Evaluation · Vector DBs
Agentic AI
Multi-agent systems · LangGraph · AutoGen · Tool use · Memory · Planning loops
Data Engineering
Medallion (Bronze/Silver/Gold) · Databricks · Delta Lake · Structured & unstructured data
Data Integration
Informatica IDMC · MDM · Data quality · Lineage · Cloud connectors
KEY RESPONSIBILITIES
- Design and deploy GenAI solutions — RAG pipelines, LLM-powered workflows, and enterprise AI assistants — across Copilot Studio, ChatGPT Enterprise, and AWS Bedrock depending on client infrastructure.
- Build Agentic AI systems using multi-agent frameworks (LangGraph, AutoGen, Agents for Bedrock) enabling autonomous reasoning, tool use, memory, and multi-step planning.
- Develop and maintain medallion architecture (Bronze → Silver → Gold) on Databricks / Delta Lake, handling structured data (CRM, sales, clinical) and unstructured data (documents, PDFs, clinical notes, emails).
- Implement Informatica IDMC pipelines for data ingestion, data quality, MDM, and lineage — integrating cloud and on-prem pharma data sources into unified data layers.
- Build Microsoft Copilot Studio copilots with custom connectors, Power Platform workflows, and Teams-based enterprise deployment for Microsoft-stack clients.
- Deploy ChatGPT Enterprise at scale — including prompt governance frameworks, usage policies, API integrations, and data privacy controls.
- Leverage AWS Bedrock for foundation model APIs, knowledge base-backed RAG, Agents for Bedrock orchestration, and Guardrails for responsible AI.
- Build semantic retrieval and vector search layers (Pinecone, OpenSearch, Azure AI Search, Bedrock Knowledge Bases) to power intelligent agents.
- Define and enforce data governance, cataloguing, and lineage standards through IDMC and Databricks Unity Catalog.
- Engage pharma clients in Japan — conducting requirements workshops, solution demos, and delivery reviews in Japanese.
- Champion responsible AI practices: agent safety, guardrails, model observability, and prompt governance across all platform deployments.
REQUIRED QUALIFICATIONS
- 6–8 years in AI/ML engineering, data engineering, or applied AI — with at least 2 years on GenAI or LLM-based production systems.
- Hands-on experience with at least two of: Microsoft Copilot Studio, ChatGPT Enterprise / OpenAI API, AWS Bedrock — with working familiarity across all three.
- Proven experience designing Agentic AI systems: multi-agent orchestration, tool-use patterns, memory management, and planning with LangGraph, AutoGen, or equivalent.
- Deep proficiency with Databricks (Delta Lake, MLflow, Unity Catalog) and medallion architecture for structured and unstructured data.
- Hands-on Informatica IDMC experience — CDI, CAI, data quality rules, MDM, or data lineage workflows.
- Strong Python skills; proficiency with LangChain, LangGraph, LlamaIndex, or similar AI orchestration frameworks.
- Experience processing unstructured data: document parsing, NLP pipelines, OCR, or embedding generation.
PREFERRED QUALIFICATIONS
- Working knowledge of pharma functions — commercial operations, R&D, sales force analytics, or medical affairs.
- Experience with pharma data platforms: Veeva CRM, IQVIA, Symphony Health, or equivalent.
- Cloud certifications: AWS ML Specialty / Solutions Architect, Azure AI Engineer, or Databricks certifications.
- Familiarity with Copilot Studio governance, Power Automate flows, and Microsoft 365 Copilot extensibility.
- Experience with AWS SageMaker, Azure OpenAI Service, or Azure Data Factory in production environments.
GENAI & AGENTIC AI SKILLS
ChatGPT Enterprise
OpenAI API / GPT-4o
AWS Bedrock
Copilot Studio
RAG pipelines
LangGraph
AutoGen
Agents for Bedrock
Prompt engineering
Vector databases
Multi-agent orchestration
Fine-tuning
DATA PLATFORM SKILLS
Databricks
Delta Lake
Medallion architecture
Informatica IDMC
Structured data
Unstructured data
MLflow
MDM / Data lineage
CLOUD & INTEGRATION
AWS SageMaker
AWS Lambda
Azure OpenAI
Power Platform
Azure Data Factory
Python
LangChain
Axtria is an equal opportunity employer and is committed to creating an inclusive environment for all employees.