XenonStack is the fastest-growing data and AI foundry for agentic systems, enabling people and organizations to gain real-time and intelligent business insights.
Agentic Systems for AI Agents
: akira.ai
Vision AI Platform
: xenonstack.ai
Inference AI Infrastructure for Agentic Systems
: nexastack.ai
THE OPPORTUNITY
-------------------
We are seeking an
AI Interaction Engineer
to design, implement, and optimize the way AI agents understand, respond, and act within enterprise workflows.
This role combines
prompt engineering
,
context orchestration
, and
memory architecture
to ensure large language models (LLMs) and multi-agent systems operate with high accuracy, reliability, and compliance.
If you are passionate about making AI
smarter, context-aware, and enterprise-ready
, and you thrive at the intersection of
linguistics, AI systems, and user experience
, this role is for you.
RESPONSIBILITIES
--------------------
Design, test, and refine
prompt strategies
to drive optimal AI behavior across diverse use cases.
Build
and context allocation for long-running, multi-turn, and multi-agent workflows.
Develop reusable
interaction templates
and
context blueprints
for product and engineering teams.
Collaborate with ML, product, and domain experts to align AI interactions with enterprise and regulatory requirements.
Implement
guardrails
for safety, compliance, tone, and brand alignment.
Leverage
execution traces, user feedback, and automated evaluation
to continuously improve AI interactions.
Conduct
A/B testing
on prompt and context variations to measure impact on accuracy, latency, and cost.
Maintain a
central library
of tested interaction patterns and context management strategies.
Stay updated on
state-of-the-art AI interaction techniques
and emerging multi-agent orchestration frameworks.
SKILLS & QUALIFICATIONS
----------------------------
Must-Have:
3-5 years in AI/ML engineering, NLP, or enterprise software development.
Strong understanding of
LLM architectures
,
prompt engineering
, and
context window limitations
.
Hands-on experience with
RAG pipelines
,
vector databases
, and
knowledge graph integration
.
Proficiency in Python and AI orchestration frameworks (LangChain, LangGraph, LlamaIndex).
Familiarity with
enterprise AI governance, privacy, and compliance
standards.
Proven ability to translate business objectives into
structured AI interactions
.
Good-to-Have:
Experience with
multi-agent orchestration
(MCP, A2A messaging, AgentBridge).
Knowledge of
reinforcement learning
(RLHF, RLAIF, reward modeling).
Exposure to
edge AI deployment
and quantized inference.
Domain knowledge in
BFSI, GRC, SOC, or FinOps
.
CAREER GROWTH & BENEFITS
-----------------------------
Continuous Learning & Growth
Certification sponsorships in AI interaction design, prompt optimization, and context engineering.
Hands-on experience with enterprise-scale
agentic AI deployments
.
Recognition & Rewards
Incentives for innovation in AI interaction design and context optimization.
Fast-track opportunities into
AI Systems Architecture
or
Model Ops
leadership roles.
Work Benefits & Well-Being
Comprehensive medical insurance and project-based allowances.
Cab facilities for women employees and special project perks.