Mentor and coach junior and mid-level engineers, conducting substantive code reviews and contributing to team technical growth.
Champion AI governance standards — security, data access, PII, prompt injection, and responsible deployment practices.
Proactively identify and resolve technical risks and architectural gaps; communicate escalations clearly to engineering leadership.
Contribute to hiring processes — interviewing candidates and helping define role expectations.
Lead knowledge-sharing sessions and represent the team in technical discussions with senior stakeholders.
You will work within the following core technology environment
Cloud Platform: AWS (S3, EC2, Lambda, SageMaker, Bedrock, IAM)
Data Warehouse: Snowflake (Snowpark, virtual warehouses, stages, streams)
Analytics & BI: ThoughtSpot
Search & Vector: OpenSearch, pgvector (Postgres)
LLM Providers: OpenAI, Anthropic / Claude, AWS Bedrock
AI Connectivity: Model Context Protocol (MCP) servers and integrations
Version Control & Project Tooling: Bitbucket, Jira, Confluence
Dev Tooling: Docker, Python, AI coding assistants (Cursor, GitHub Copilot, Claude Code)
Bachelor's degree or higher in Artificial Intelligence, Computer Science, Data Science, Software Engineering, or a related technical field (or equivalent practical experience).
At least 4 years of professional experience in software engineering or AI engineering, including at least 2–3 years designing, developing, and deploying AI/ML solutions in production environments.
Strong expertise in agentic AI systems, including multi-agent architectures, orchestration frameworks (e.g., LangGraph, AutoGen), and state management.
Experience designing secure enterprise AI integrations using MCP or similar integration patterns.
Advanced knowledge of vector search and RAG architectures using OpenSearch (kNN, hybrid search) and pgvector.
Hands-on experience with LLM fine-tuning (LoRA, PEFT, RLHF) and selecting appropriate approaches across fine-tuning, prompt engineering, and RAG.
Advanced experience with AWS AI services (including SageMaker and Bedrock) and Snowflake (Snowpark, Dynamic Tables, feature pipelines) for scalable AI solutions.
Experience implementing AI evaluation, observability, security, governance, and compliance frameworks for production systems.
Strong cloud engineering skills, including containers, Infrastructure as Code (IaC), CI/CD, and cost-optimized deployments.
Excellent communication, stakeholder management, and mentoring skills, with the ability to influence technical decisions across cross-functional teams.
We offer
The opportunity to build production-grade AI solutions using the latest LLM, RAG, and agentic AI technologies on AWS and Snowflake.
A collaborative, international environment where you'll work closely with software engineers, solution architects, and business stakeholders to solve real-world challenges.
Exposure to modern AI tooling, cloud-native architectures, and the freedom to influence technical design and engineering best practices.
A hybrid work model with flexible working hours.
A benefits package, including private health insurance, medical care, and a Multisport card.
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