ai engineer.
We're looking for a highly skilled AI Engineer to design, develop and deploy advanced AI/ML systems. This role is centered on building next-generation agentic AI solutions powered by retrieval-augmented generation (RAG), using modern orchestration frameworks including LangGraph and Model Context Protocol (MCP). You'll bring deep expertise in Python-based AI development, agentic system design and Model Risk Management to deliver production-grade AI systems that are both innovative and trustworthy.
function
Artificial intelligence / machine learning
type
Full-time / contract
level
Senior (5+ yrs, master's preferred)
primary stack
Python, langgraph, rag, mcp
what you'll do.
Four buckets of ownership, from agent design through governance and research.
01
Agentic AI Development
→
Design and build agentic AI systems capable of reasoning, planning, tool usage and executing complex multi-step workflows
→
Develop and maintain RAG and Graph RAG architectures from retrieval layer through generation and evaluation
→
Implement agent orchestration using LangGraph and Model Context Protocol (MCP)
→
Integrate large language models with internal data sources, APIs, and enterprise tooling
02
ML/AI Engineering
→
Build, fine-tune, and evaluate LLMs and NLP models for production use cases
→
Optimize model performance, latency and cost at production scale
→
Develop end-to-end ML pipelines from data preparation through model deployment and monitoring
→
Apply NLP techniques including semantic search, information extraction, classification and summarization
03
Model Risk Management
→
Govern AI systems for explainability, auditability and regulatory compliance
→
Develop and maintain model documentation, validation frameworks and risk assessments aligned with MRM standards
→
Ensure AI systems meet enterprise risk and compliance requirements before production deployment
→
Partner with risk, compliance and legal stakeholders to align AI governance practices
04
Collaboration & Research
→
Collaborate with data scientists, ML engineers and product teams across the model lifecycle
→
Stay current on advancements in LLMs, agentic frameworks and RAG architectures
→
Contribute to internal knowledge sharing, documentation, and best practices
what we're looking for.
the bonus list makes you stand out
Required
✓
Master's degree in Computer Science, Artificial Intelligence or a related field
✓
5+ years of hands-on Python dev - focus on AI/ML systems
✓
Deep experience building RAG systems and Graph RAG architectures end to end
✓
Hands-on experience with agentic AI frameworks (LangGraph, LangChain or equivalent)
✓
Strong background in NLP including semantic search, classification and information extraction
✓
Demonstrated experience with Model Risk Management (MRM) frameworks and AI governance
✓
Familiarity with Model Context Protocol (MCP) for agent-tool integration
Nice to have
+
Experience with vector databases (Pinecone, Weaviate, pgvector, Chroma)
+
Exposure to MLOps tooling and model deployment pipelines (MLflow, Kubeflow, SageMaker)
+
Background in financial services AI compliance or regulated industry AI governance
+
Contributions to open source AI/ML projects
tech stack snapshot.
a quick scan of everything you'll touch day to day
primary skills
Artificial Intelligence
ML
secondary skill
Python
tertiary skill
NLP
agentic frameworks
LangGraph
LangChain
Model Context Protocol (MCP)
rag architecture
RAG
embedding pipelines
Graph RAG
vector retrieval
NLP / ML
LLMs
extraction
classification
Transformers
semantic search
governance
Model Risk Management (MRM)
explainability
auditability
ML ops
SageMaker
MLflow
Kubeflow (preferred)
vector stores
Pinecone
Weaviate
Chroma (preferred)
pgvector