We are seeking a Software Engineer with 5+ years of experience to help design and build AI-driven automation solutions for internal customer engagement and onboarding into enterprise cryptographic services.
This role focuses on developing Python-based, cloud-native applications that leverage natural language processing, GenAI/LLMs, and agentic workflows to automate manual analysis, understand customer intent, and guide users to the appropriate cryptographic services.
The ideal candidate has hands-on experience with AWS, modern AI/ML architectures, and containerized microservices, and enjoys working on end-to-end systems spanning data ingestion, AI orchestration, and secure cloud infrastructure.
Key Responsibilities
Design and develop
Python-based applications and services
to automate internal customer engagement and onboarding workflows.
Implement
GenAI solutions
using LLMs, embeddings, vector databases, and
retrieval-augmented generation (RAG)
architectures.
Build
NLP-enabled agents
that can understand natural-language requests and assist users in selecting the appropriate cryptographic services.
Automate
manual analysis and triage processes
using AI/ML and rule-based logic.
Develop and maintain
cloud-native microservices
using AWS services such as Lambda, ECS/EKS, S3, DynamoDB, API Gateway, and Bedrock.
Build and manage
knowledge bases , embedding pipelines, and contextual retrieval systems for accurate, reliable responses.
Design
agentic workflows
and orchestration logic to support decision-making and process automation.
Collaborate with product, data, and engineering teams to translate business and compliance requirements into technical solutions.
Monitor, troubleshoot, and optimize production workloads for performance, reliability, and cost efficiency.
Required Skills & Experience
5+ years of professional experience
in software engineering, cloud engineering, or AI/ML development.
Strong proficiency in Python (experience with FastAPI, Flask, or similar frameworks preferred).
Hands-on experience with AWS cloud services, especially serverless and container-based architectures.
Experience with Docker, GitHub, and CI/CD automation.
Working knowledge of LLMs, prompt engineering, embeddings, and GenAI concepts.
Experience building or contributing to RAG pipelines or search/knowledge-based AI systems.
Familiarity with vector databases (e.g., Pinecone, FAISS, Redis, OpenSearch, or similar).
Exposure to agentic frameworks (e.g., LangChain, LlamaIndex, or custom agent implementations).
Understanding of API development, distributed systems, and cloud-native design patterns.
Experience integrating with AWS Bedrock or other foundation model platforms.
Exposure to security-focused or regulated environments (financial services, cryptography, compliance).
Familiarity with infrastructure-as-code tools such as CloudFormation or CDK.
Experience working on internal enterprise platforms rather than consumer applications.