Overview
We are seeking a
Lead Software Engineer
with deep expertise in
full stack development ,
Generative AI , and
DevSecOps practices .
This is a hands-on leadership role at the forefront of building
AI-native products
that combine robust engineering with cutting-edge generative capabilities.
You will drive the architecture, development, and deployment of secure, scalable applications infused with AI — from
LLM-powered experiences and intelligent agents
to
automated workflows and decisioning systems .
Beyond technical execution, you will embed
shift-left quality and security , mentor engineers, and set the direction for how Generative AI shapes our product strategy.
Key Responsibilities
Generative AI Engineering
Design and build
LLM-powered applications
(retrieval-augmented generation, multi-agent systems, generative search, AI copilots).
Implement
prompt engineering, fine-tuning, and evaluation frameworks
for production-grade reliability.
Optimize AI inference at scale (latency, cost efficiency, guardrails).
Ensure
responsible AI use
(bias detection, explainability, data privacy).
Full Stack Development
Architect and deliver end-to-end features across
frontend (React/Next.js) ,
backend (Node.js/Python/Java) , and
cloud-native infrastructure .
Build secure APIs, data pipelines, and real-time services that power AI-driven user experiences.
DevSecOps & Shift-Left Practices
Embed security and testing early in the development lifecycle.
Own
CI/CD pipelines
with automated testing, vulnerability scanning, and compliance guardrails.
Implement
Infrastructure as Code (IaC)
and observability for resilience and scalability.
Leadership & Collaboration
Mentor engineers on AI software engineering best practices.
Collaborate with product managers and data scientists to translate
business problems into AI-first solutions .
Drive technical decision-making, balancing innovation speed with long-term system sustainability.
Qualifications
Must-Have:
7+ years of professional software engineering experience
Strong
full stack development skills (React/Next.js, Node.js, Python, Java, etc.)
Proven experience delivering
Generative AI applications
into production, including:
o
LLM integration
( OpenAI , Anthropic, Llama, etc.)
RAG
pipelines (vector databases, embeddings, semantic search)
Fine-tuning and evaluation of models
Safety/guardrail implementation
Solid knowledge of
DevSecOps and shift-left practices
(automated testing, SAST/DAST, IaC, CI/CD).
Hands-on experience with
cloud-native platforms
(AWS, GCP, Azure) and container orchestration (Docker, Kubernetes) and infrastructure provisioning.
Strong foundation in
software architecture and distributed systems .
Nice-to-Have:
Experience with
multi-agent architectures
or autonomous AI workflows.
Familiarity with
AI evaluation frameworks
(LangSmith, Weights & Biases, MLflow).
Contributions to open-source AI/ML or developer tooling.
Startup or high-growth environment experience.
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